End of life detection for analyte sensors

ABSTRACT

Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 14/523,323, filed Oct. 24, 2014, which is a continuation of U.S.application Ser. No. 13/733,742, filed Jan. 3, 2013. The aforementionedapplications are incorporated by reference herein in their entirety, andare hereby expressly made a part of this specification.

TECHNICAL FIELD

The embodiments described herein relate generally to systems and methodsfor processing sensor data from continuous analyte sensors and fordetection of end of life of the sensors.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which can cause anarray of physiological derangements associated with the deterioration ofsmall blood vessels, for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye. A hypoglycemic reaction (lowblood sugar) can be induced by an inadvertent overdose of insulin, orafter a normal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricks to obtain blood samples for measurement. Due to the lack ofcomfort and convenience associated with finger pricks, a person withdiabetes normally only measures his or her glucose levels two to fourtimes per day. Unfortunately, time intervals between measurements can bespread far enough apart that the person with diabetes finds out too lateof a hyperglycemic or hypoglycemic condition, sometimes incurringdangerous side effects. It is not only unlikely that a person withdiabetes will take a timely SMBG value, it is also likely that he or shewill not know if his or her blood glucose value is going up (higher) ordown (lower) based on conventional methods. Diabetics thus may beinhibited from making educated insulin therapy decisions.

Another device that some diabetics use to monitor their blood glucose isa continuous analyte sensor. A continuous analyte sensor typicallyincludes a sensor that is placed subcutaneously, transdermally (e.g.,transcutaneously), or intravascularly. The sensor measures theconcentration of a given analyte within the body, and generates a rawsignal that is transmitted to electronics associated with the sensor.The raw signal is converted into an output value that is displayed on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful information, such as blood glucose expressed in mg/dL.

One of the major perceived benefits of a continuous analyte sensor isthe ability of these devices to be used continuously for a number ofdays, e.g., 1, 3, 5, 6, 7, 9, 10, 14 days or more. While these variousdevices may have been approved for a certain number of days, andsometimes are used “off label” beyond their approved number of days, theperformance of the sensors are known to degrade over the lifetime. And,because no environment is the same for any two sensors, the lifetime ofany particular sensor may in actuality be less than the approvedlifetime of the sensor. Consequently, it would be beneficial to know thestatus or time for which the end of life of a sensor is near, so thatthe user may be informed that the sensor should be changed.

SUMMARY

The present systems and methods relate to processing analyte sensordata. The systems and methods enable intelligence in the determinationof performance issues related to reference data being entered into thesensor and the sensor itself, in response to which, appropriate actionmay be taken by the system or prompted to be taken by the user.

In a first aspect, a method is provided for determining an end of lifeof a continuous analyte sensor, comprising: evaluating a plurality ofrisk factors associated with end of life symptoms of a sensor;determining an end of life status of the sensor based on the evaluationof the plurality of risk factors; and providing an output related to theend of life status of the sensor, wherein the plurality of risk factorscomprise at least two risk factors selected from the group consisting ofa number of days the sensor has been in use, a rate of change of sensorsensitivity, end of life noise, oxygen concentration, glucose patterns,error between reference values, and sensor values in clinical units.

In an embodiment of the first aspect, one of the at least two riskfactors comprises a number of days the sensor has been in use, andwherein evaluating a number of days the sensor has been in use comprisesat least one of evaluating an amount of time since the sensor has beeninitialized, evaluating an amount of time since the sensor has beenimplanted, or evaluating an amount of time since the sensor has beeninitially calibrated.

In an embodiment of the first aspect, one of the at least two riskfactors comprises a rate of change of sensor sensitivity, and whereinevaluating a rate of change of sensor sensitivity comprises evaluatingat least one of a direction of rate of change of sensor sensitivity, anamplitude of rate of change of sensor sensitivity, a derivative of rateof change of sensor sensitivity or a comparison of the rate of change ofsensor sensitivity to a priori rate of change sensitivity information.

In an embodiment of the first aspect, one of the at least two riskfactors comprises end of life noise, and wherein evaluating end of lifenoise comprises evaluating at least one of duration of noise, amagnitude of noise, a history of noise, a spectral content of a signalfrom the sensor, spikes in the signal from the sensor, skewness of thesignal of the sensor or noise patterns by pattern recognitionalgorithms.

In an embodiment of the first aspect, one of the at least two riskfactors comprises end of life noise, and wherein evaluating end of lifenoise comprises evaluating at least two of duration of noise, amagnitude of noise, a history of noise, a spectral content of a signalfrom the sensor, spikes in the signal from the sensor, skewness of thesignal of the sensor or noise patterns by pattern recognitionalgorithms.

In an embodiment of the first aspect, one of the at least two riskfactors comprises glucose patterns, and wherein evaluating glucosepatterns comprises evaluating at least one of mean glucose, glucosevariability, peak-to-peak glucose excursions, or expected versusunexpected glucose trends based on timing.

In an embodiment of the first aspect, one of the at least two riskfactors comprises error between reference values and sensor values inclinical units, and wherein evaluating error between reference valuesand sensor values in clinical units comprises evaluating at least one ofa direction of error between reference values and sensor values inclinical units, a linearity of the sensor, or an error at calibration.

In an embodiment of the first aspect, the evaluating a plurality of riskfactors comprises translating outputs of the plurality of risk factorevaluations to end of life risk factor values.

In an embodiment of the first aspect, translating an output of end oflife risk factor values comprises determining a likelihood of recovery.

In an embodiment of the first aspect, the determining an end of lifestatus comprises combining the end of life risk factor values into acombined end of life score.

In an embodiment of the first aspect, the determining an end of lifestatus is based on the combined end of life score.

In an embodiment of the first aspect, combining the end of life riskfactor values into a combined end of life score comprises weighting oneor more of the plurality of risk factors.

In an embodiment of the first aspect, each of the plurality of riskfactors is partially indicative of the end of life of the sensor basedon a comparison of the risk factor to one or more criteria.

In an embodiment of the first aspect, if at least two of the pluralityof risk factors are determined to meet the one or more criteria,respectively, then the combination of the at least two variables isindicative of the end of life of the sensor.

In an embodiment of the first aspect, determining the end of life statuscomprises using a probability analysis, a decision fusion, lineardiscriminant analysis or fuzzy logic.

In an embodiment of the first aspect, the output related to the end oflife status is displayed on a user interface.

In an embodiment of the first aspect, the output related to the end oflife status comprises instructions to change the sensor.

In an embodiment of the first aspect, the output related to the end oflife status comprises a data transmission.

In an embodiment of the first aspect, the method is implemented on acomputer having a processor and a memory coupled to said processor,wherein at least one of the evaluating, the determining, and theproviding is performed using the processor.

In a second aspect, a system is provided for determining an end of lifeof a continuous analyte sensor, the system comprising sensor electronicsconfigured to be operably connected to a continuous analyte sensor, thesensor electronics configured to: evaluate a plurality of risk factorsassociated with end of life symptoms of a sensor; determine an end oflife status of the sensor based on the evaluation of the plurality ofrisk factors; and provide an output related to the end of life status ofthe sensor, wherein the plurality of risk factors comprise at least tworisk factors selected from the group consisting of a number of days thesensor has been in use, a rate of change of sensor sensitivity, end oflife noise, oxygen concentration, glucose patterns, error betweenreference values and sensor values in clinical units.

In an embodiment of the second aspect, one of the at least two riskfactors comprises a number of days the sensor has been in use, andwherein the sensor electronics are configured to evaluate a number ofdays the sensor has been in use by at least one of evaluating an amountof time since the sensor has been initialized, evaluating an amount oftime since the sensor has been implanted, or evaluating an amount oftime since the sensor has been initially calibrated.

In an embodiment of the second aspect, one of the at least two riskfactors comprises a rate of change of sensor sensitivity, and whereinthe sensor electronics are configured to evaluate a rate of change ofsensor sensitivity by evaluating at least one of a direction of rate ofchange of sensor sensitivity, an amplitude of rate of change of sensorsensitivity, a derivative of rate of change of sensor sensitivity, or acomparison of the rate of change of sensor sensitivity to a priori rateof change sensitivity information.

In an embodiment of the second aspect, one of the at least two riskfactors comprises end of life noise, and wherein the sensor electronicsare configured to evaluate end of life noise by evaluating at least oneof duration of noise, a magnitude of noise, a history of noise, aspectral content of a signal from the sensor, spikes in the signal fromthe sensor, skewness of the signal of the sensor, or noise patterns bypattern recognition algorithms.

In an embodiment of the second aspect, one of the at least two riskfactors comprises end of life noise, and wherein the sensor electronicsare configured to evaluate end of life noise by evaluating at least twoof duration of noise, a magnitude of noise, a history of noise, aspectral content of a signal from the sensor, spikes in the signal fromthe sensor, skewness of the signal of the sensor, or noise patterns bypattern recognition algorithms.

In an embodiment of the second aspect, one of the at least two riskfactors comprises glucose patterns, and wherein the sensor electronicsare configured to evaluate glucose patterns by evaluating at least oneof mean glucose, glucose variability, peak-to-peak glucose excursions,or expected versus unexpected glucose trends based on timing.

In an embodiment of the second aspect, one of the at least two riskfactors comprises error between reference values and sensor values inclinical units, and wherein the sensor electronics are configured toevaluate error between reference values and sensor values in clinicalunits by evaluating at least one of a direction of error betweenreference values and sensor values in clinical units, or a linearity ofthe sensor and an error at calibration.

In an embodiment of the second aspect, the sensor electronics areconfigured to evaluate a plurality of risk factors by translatingoutputs of the plurality of risk factor evaluations to end of life riskfactor values.

In an embodiment of the second aspect, the sensor electronics areconfigured to translate an output of end of life risk factor values bydetermining a likelihood of recovery.

In an embodiment of the second aspect, the sensor electronics areconfigured to determine an end of life status by combining the end oflife risk factor values into a combined end of life score.

In an embodiment of the second aspect, the sensor electronics areconfigured to determine an end of life status based on the combined endof life score.

In an embodiment of the second aspect, the sensor electronics areconfigured to combine the end of life risk factor values into a combinedend of life score by weighting one or more of the plurality of riskfactors.

In an embodiment of the second aspect, each of the plurality of riskfactors is partially indicative of the end of life of the sensor basedon a comparison of the risk factor to one or more criteria.

In an embodiment of the second aspect, if at least two of the pluralityof risk factors are determined to meet the one or more criteria,respectively, then a combination of the at least two variables isindicative of the end of life of the sensor.

In an embodiment of the second aspect, the sensor electronics areconfigured to determine the end of life status by using a probabilityanalysis, a decision fusion, linear discriminant analysis, or fuzzylogic.

In an embodiment of the second aspect, the output related to the end oflife status is displayed on a user interface.

In an embodiment of the second aspect, the output related to the end oflife status comprises instructions to change the sensor.

In an embodiment of the second aspect, the output related to the end oflife status comprises a data transmission.

In an embodiment of the second aspect, the sensor electronics comprise aprocessor module, the processor module comprising instructions stored incomputer memory, wherein the instructions, when executed by theprocessor module, cause the sensor electronics to perform theevaluating, the determining and the providing.

In a third aspect, a method is provided for determining if a continuousanalyte sensor has been reused, comprising: evaluating a plurality ofrisk factors associated with end of life symptoms of a sensor;determining an end of life status of the sensor by performing an end oflife function based on the evaluation of the plurality of risk factors;and providing an output related to a sensor reuse within a predeterminedtime frame after sensor initialization if the end of life status meetsone or more predetermined sensor reuse criteria, wherein the pluralityof risk factors comprise at least two risk factors selected from thegroup consisting of a number of days the sensor has been in use, a rateof change of sensor sensitivity, end of life noise, oxygenconcentration, glucose patterns, error between reference values, andsensor values in clinical units.

In an embodiment of the third aspect, the providing an output comprisesdisabling display of sensor data responsive to the end of life statusmeeting the one or more predetermined sensor reuse criteria.

In a fourth aspect, a system is provided for determining if a continuousanalyte sensor has been reused, the system comprising sensor electronicsconfigured to be operably connected to a continuous analyte sensor, thesensor electronics configured to: evaluate a plurality of risk factorsassociated with end of life symptoms of the sensor; determine an end oflife status of the sensor by performing an end of life function based onthe evaluation of the plurality of risk factors; and provide an outputrelated to the sensor reuse of the sensor within a predetermined timeframe after sensor initialization if the end of life status meets one ormore predetermined sensor reuse criteria, wherein the plurality of riskfactors comprise at least two risk factors selected from the groupconsisting of a number of days the sensor has been in use, a rate ofchange of sensor sensitivity, end of life noise, oxygen concentration,glucose patterns, error between reference values, and sensor values inclinical units.

In an embodiment of the fourth aspect, the providing an output comprisesdisabling display of sensor data responsive to the end of life statusmeeting the one or more predetermined sensor reuse criteria.

In an embodiment of the fourth aspect, the sensor electronics comprise aprocessor module, the processor module comprising instructions stored incomputer memory, wherein the instructions, when executed by theprocessor module, cause the sensor electronics to perform the evaluatingand the providing.

In a fifth aspect, a method is provided for detecting outliers inanalyte sensor data, comprising: iteratively evaluating a plurality ofsubsets of a calibration data set to determine a best subset;identifying a boundary or confidence interval associated with the bestsubset; identifying values outside the boundary or confidence intervalas possible outliers; evaluating the relevancy of the possible outliersto determine outlier information; and processing responsive to theoutlier information.

In an embodiment of the fifth aspect, the evaluation of subsets todetermine a best subset includes generating a regression line or aconvex hull.

In an embodiment of the fifth aspect, the regression line is generatedusing at least ½ of data points in the calibration set.

In an embodiment of the fifth aspect, the evaluating the relevancy ofthe possible outliers to determine outlier information comprises atleast one of evaluating the clinical relevancy of the possible outliers,discrimination of the root cause of the error in the possible outliers,and trends of the outlier information.

In an embodiment of the fifth aspect, the evaluating the relevancycomprises examining one or more factors from the following list: theamplitude of error of a data point relative to the best line, thedirection of error a data point relative to the best line, a clinicalrisk of the data at a time stamp of the data point, a rate of change ofthe analyte concentration or derivative of the sensor data associatedwith a data point, a rate of acceleration or deceleration of the analyteconcentration or second derivative of the sensor data associated withthe data point.

In an embodiment of the fifth aspect, the processing responsive to theoutlier information comprises removing the outlier temporarily orpermanently from the calibration set, prospectively or retrospectively.

In an embodiment of the fifth aspect, the processing responsive to theoutlier information comprises flagging an outlier and keeping theoutlier in the calibration data set until the next data point iscollected.

In a sixth aspect, a system is provided for detecting outliers inanalyte sensor data, the system comprising sensor electronics configuredto be operably connected to a continuous analyte sensor, the sensorelectronics configured to: iteratively evaluate a plurality of subsetsof a calibration data set to determine a best subset; identify aboundary or confidence interval associated with a best subset; identifyvalues outside the boundary or confidence interval as possible outliers;evaluate the relevancy of the possible outliers to determine outlierinformation; and process responsive to the outlier information.

In an embodiment of the sixth aspect, the evaluation of subsets todetermine a best subset includes generating a regression line or aconvex hull.

In an embodiment of the sixth aspect, the regression line is generatedusing at least ½ of data points in the calibration set.

In an embodiment of the sixth aspect, the evaluating the relevancy ofthe possible outliers to determine outlier information comprises atleast one of evaluating the clinical relevancy of the possible outliers,discrimination of the root cause of the error in the possible outlier,and trends of outlier information.

In an embodiment of the sixth aspect, the evaluating the relevancycomprises examining one or more factors from the following list: theamplitude of error of a data point relative to the best line, thedirection of error a data point relative to the best line, a clinicalrisk of the data at a time stamp of the data point, a rate of change ofthe analyte concentration or derivative of the sensor data associatedwith a data point, a rate of acceleration or deceleration of the analyteconcentration or second derivative of the sensor data associated withthe data point.

In an embodiment of the sixth aspect, the processing comprises removingthe outlier temporarily or permanently from the calibration set,prospectively or retrospectively.

In an embodiment of the sixth aspect, the processing comprises flaggingan outlier and keeping the outlier in the calibration data set until thenext data point is collected.

In an embodiment of the sixth aspect, the sensor electronics comprise aprocessor module, the processor module comprising instructions stored incomputer memory, wherein the instructions, when executed by theprocessor module, cause the sensor electronics to perform the evaluatingand the processing.

In a seventh aspect, a method is provided for detecting outliers inanalyte sensor data, comprising: iteratively evaluating a plurality ofsubsets of a calibration data set; identifying a possible outlier basedon one or more first outlier criteria; evaluating the relevancy of thepossible outlier based on one or more relevancy criteria to discriminatea root case of the possible outlier; and processing outlier informationresponsive thereto.

In an embodiment of the seventh aspect, the evaluating the relevancycomprises evaluating at least one of: time since sensor implant, trendsin outlier evaluation, the amplitude of error of a data point relativeto the best line, the direction of error a data point relative to thebest line, a clinical risk of the data at a time stamp of the datapoint, a rate of change of the analyte concentration or derivative ofthe sensor data associated with a data point, a rate of acceleration ordeceleration of the analyte concentration or second derivative of thesensor data associated with the data point.

In an eighth aspect, a system is provided for detecting outliers inanalyte sensor data, the system comprising sensor electronics configuredto be operably connected to a continuous analyte sensor, the sensorelectronics configured to: iteratively evaluate a plurality of subsetsof a calibration data set; identify a possible outlier based on one ormore first outlier criteria; evaluate the relevancy of the possibleoutlier based on one or more relevancy criteria to discriminate a rootcase of the possible outlier; and process outlier information responsivethereto.

In an embodiment of the eighth aspect, the evaluating the relevancycomprises evaluating at least one of: time since sensor implant, trendsin outlier evaluation, the amplitude of error of a data point relativeto the best line, the direction of error a data point relative to thebest line, a clinical risk of the data at a time stamp of the datapoint, a rate of change of the analyte concentration or derivative ofthe sensor data associated with a data point, a rate of acceleration ordeceleration of the analyte concentration or second derivative of thesensor data associated with the data point.

In an embodiment of the eighth aspect, the sensor electronics comprise aprocessor module, the processor module comprising instructions stored incomputer memory, wherein the instructions, when executed by theprocessor module, cause the sensor electronics to perform the evaluatingand the processing.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present disclosure, both as to its structure andoperation, may be understood in part by study of the accompanyingdrawings, in which like reference numerals refer to like parts. Thedrawings are not necessarily to scale, emphasis instead being placedupon illustrating the principles of the disclosure.

FIG. 1A illustrates a schematic diagram of sensor sensitivity as afunction of time during a sensor session, in accordance with anembodiment;

FIG. 1B illustrates schematic representations of sensitivity andbaseline derived from a regression at different time periods of a sensorsession, in accordance with the embodiment of FIG. 1A.

FIGS. 2A-2B and FIGS. 2C-2D collectively illustrate differentembodiments of processes for generating a sensor sensitivity profile.

FIG. 3 illustrates a diagram showing different types of information thatcan be input into the sensor system to define the sensor sensitivityprofile over time.

FIG. 4 illustrates a schematic diagram of sensor sensitivity as afunction of time between completion of sensor fabrication and the startof the sensor session, in accordance with an embodiment.

FIG. 5 illustrates a diagram of a calibration process that uses variousinputs to form a transformation function in accordance with anembodiment.

FIG. 6 illustrates a flowchart describing a process for dynamically andintelligently determining or detecting outlier matched data pairs inaccordance with an embodiment.

FIG. 7 illustrates a diagram of a calibration set having 6 matched datapairs in accordance with an embodiment.

FIG. 8 illustrates a diagram of a calibration line drawn using OrdinaryLeast Squares (“OLS”) in accordance with an embodiment.

FIG. 9 illustrates a flowchart describing a process for dynamically andintelligently determining end of life for a sensor in accordance with anembodiment.

FIG. 10 illustrates a flowchart showing example inputs and outputs forthe flowchart of FIG. 9.

FIG. 11A shows a sensor signal with a loss of sensitivity (downwardslope of signal) showing end of life symptoms. FIG. 11B illustrates theuse of a spike detector to detect an increase in downward spikes in asignal produced by a sensor, which is a specific risk factor for end oflife.

FIG. 12A shows the power spectral density (PSD) of the sensor signalshown in 11A illustrating end of life symptoms. FIG. 12B shows the sametype of graph as 12A, but with from a sensor signal that did not exhibitend of life symptoms.

FIG. 13A illustrates a translation function that maps a noise durationto an end of life risk factor value in one embodiment. FIG. 13Billustrates a translation function that maps the error at calibration toan end of life risk factor value in one embodiment.

FIG. 13C illustrates a translation function that may be used by theprocessor module to translate the combined EOL index calculated from thevarious risk factors to an end of life score and/or end of life status,in one embodiment.

FIG. 14 illustrates a flowchart describing a process for determiningsensor reuse associated in accordance with an embodiment.

FIG. 15 illustrates a diagram showing noise duration associated with endof life in accordance with an embodiment.

DETAILED DESCRIPTION Definitions

In order to facilitate an understanding of the embodiments describedherein, a number of terms are defined below.

The term “analyte,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is are not to be limited to a special or customized meaning),and refers without limitation to a substance or chemical constituent ina biological fluid (for example, blood, interstitial fluid, cerebralspinal fluid, lymph fluid or urine) that can be analyzed. Analytes mayinclude naturally occurring substances, artificial substances,metabolites, and/or reaction products. In some embodiments, the analytefor measurement by the sensor heads, devices, and methods disclosedherein is glucose.

The terms “continuous analyte sensor,” and “continuous glucose sensor,”as used herein, are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to a device that continuously or continually measures aconcentration of an analyte/glucose and/or calibrates the device (e.g.,by continuously or continually adjusting or determining the sensor'ssensitivity and background), for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

The terms “raw data stream” and “data stream,” as used herein, are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to ananalog or digital signal directly related to the analyte concentrationmeasured by the analyte sensor. In one example, the raw data stream isdigital data in counts converted by an A/D converter from an analogsignal (for example, voltage or amps) representative of an analyteconcentration. The terms broadly encompass a plurality of time spacedmatched data pairs from a substantially continuous analyte sensor, whichcomprises individual measurements taken at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes or longer.

The terms “sensor data”, as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any dataassociated with a sensor, such as a continuous analyte sensor. Sensordata includes a raw data stream, or simply data stream, of analog ordigital signal directly related to a measured analyte from an analytesensor (or other signal received from another sensor), as well ascalibrated and/or filtered raw data. In one example, the sensor datacomprises digital data in “counts” converted by an A/D converter from ananalog signal (e.g., voltage or amps) and includes one or more datapoints representative of a glucose concentration. Thus, the terms“sensor data point” and “data point” refer generally to a digitalrepresentation of sensor data at a particular time. The terms broadlyencompass a plurality of time spaced data points from a sensor, such asa from a substantially continuous glucose sensor, which comprisesindividual measurements taken at time intervals ranging from fractionsof a second up to, e.g., 1, 2, or 5 minutes or longer. In anotherexample, the sensor data includes an integrated digital valuerepresentative of one or more data points averaged over a time period.Sensor data may include calibrated data, smoothed data, filtered data,transformed data, and/or any other data associated with a sensor.

The term “counts,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a unit of measurement of a digital signal.In one example, a raw data stream measured in counts is directly relatedto a voltage (for example, converted by an A/D converter), which isdirectly related to current from a working electrode.

The term “matched data pair” or “data pair” as used herein is a broadterm and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation toreference data (for example, one or more reference analyte data points)matched with substantially time corresponding sensor data (for example,one or more sensor data points).

The term “sensor electronics,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the components (for example,hardware and/or software) of a device configured to process data. In thecase of an analyte sensor, the data includes biological informationobtained by a sensor regarding the concentration of the analyte in abiological fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398describe suitable electronic circuits that can be utilized with devicesof certain embodiments.

The term “calibration” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the process of determiningthe relationship between raw sensor data (e.g., analog (nA) or digitalunits (counts) to clinically meaningful units (e.g., mg/dL or mmol/L forglucose)). In some embodiments, the process includes determining therelationship by pairing the sensor data and the corresponding referencedata, however other calibration techniques (without pairing sensor dataand time-corresponding reference data) may also be used. In someembodiments, namely, in continuous analyte sensors, calibration can beupdated or recalibrated over time (e.g., once or twice daily, or more,or when reference data is provided by the host) as changes in therelationship between the sensor data and reference data occur, forexample, due to changes in sensitivity, baseline, transport, metabolism,or the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, also referred to asa transformation function, to provide a meaningful value to a user.

The term “calibration set” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a set of data comprisinginformation useful for calibration. In some embodiments, the calibrationset is formed from one or more matched data pairs, which are used todetermine the relationship between the reference data and the sensordata; however other data derived pre-implant, externally or internallymay also be used.

The terms “sensitivity” or “sensor sensitivity,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refer without limitation to anamount of signal produced by a certain concentration of a measuredanalyte, or a measured species (e.g., H₂O₂) associated with the measuredanalyte (e.g., glucose). For example, in one embodiment, a sensor has asensitivity of from about 1 to about 300 picoAmps of current for every 1mg/dL of glucose analyte.

The term “sensitivity profile” or “sensitivity curve,” as used herein,are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and is not to belimited to a special or customized meaning), and refer withoutlimitation to a representation of a change in sensitivity over time

Overview

Conventional in vivo continuous analyte sensing technology has typicallyrelied on reference measurements performed during a sensor session forcalibration of the continuous analyte sensor. The reference measurementsare matched with substantially time corresponding sensor data to creatematched data pairs. Regression is then performed on some or all of thematched data pairs (e.g., by using least squares regression) to generatea transformation function that defines a relationship between a sensorsignal and an estimated glucose concentration.

In critical care settings, calibration of continuous analyte sensors isoften performed by using, as reference, a calibration solution with aknown concentration of the analyte. This calibration procedure can becumbersome, as a calibration bag, separate from (and an addition to) anIV (intravenous) bag, is typically used. In the ambulatory setting,calibration of continuous analyte sensors has traditionally beenperformed by capillary blood glucose measurements (e.g., a finger stickglucose test), through which reference data is obtained and input intothe continuous analyte sensor system. This calibration proceduretypically involves frequent finger stick measurements, which can beinconvenient and painful.

In certain embodiments, the continuous analyte sensor includes one ormore working electrodes and one or more reference and/or counterelectrodes, which operate together to measure a signal associated with aconcentration of the analyte in the host. The output signal from theworking electrode is typically a raw data stream that is calibrated,processed, and used to generate an estimated analyte (e.g., glucose)concentration. In certain embodiments, the continuous analyte sensor maymeasure an additional signal associated with the baseline and/orsensitivity of the sensor, thereby enabling monitoring of baselineand/or additional monitoring of sensitivity changes or drift that mayoccur in a continuous analyte sensor over time.

In some embodiments, the sensor extends through a housing, whichmaintains the sensor on the skin and provides for electrical connectionof the sensor to sensor electronics. In one embodiment, the sensor isformed from a wire. For example, the sensor can include an elongatedconductive body, such as a bare elongated conductive core (e.g., a metalwire) or an elongated conductive core coated with one, two, three, four,five, or more layers of material, each of which may or may not beconductive. The elongated sensor may be long and thin, yet flexible andstrong. For example, in some embodiments the smallest dimension of theelongated conductive body is less than about 0.1 inches, 0.075 inches,0.05 inches, 0.025 inches, 0.01 inches, 0.004 inches, or 0.002 inches.Other embodiments of the elongated conductive body are disclosed in U.S.Patent Application Publication No. 2011/0027127, which is incorporatedherein by reference in its entirety. Preferably, a membrane system isdeposited over at least a portion of electroactive surfaces of thesensor (including a working electrode and optionally a referenceelectrode) and provides protection of the exposed electrode surface fromthe biological environment, diffusion resistance (limitation) of theanalyte if needed, a catalyst for enabling an enzymatic reaction,limitation or blocking of interferants, and/or hydrophilicity at theelectrochemically reactive surfaces of the sensor interface. Disclosuresregarding the different membrane systems that may be used with theembodiments described herein are described in U.S. Patent PublicationNo. US-2009-0247856-A1, which is incorporated herein by reference in itsentirety. In addition to wire based sensors, the systems and methodsdescribed here may be applicable to other invasive, minimally invasiveor non-invasive sensor technologies, such as the planar substrate-basedsensor technologies.

Calibrating sensor data from continuous analyte sensors generallyinvolves defining a relationship between sensor-generated measurements(e.g., in units of nA or digital counts after A/D conversion) andclinically relevant values (e.g., in units of mg/dL or mmol/L). Incertain embodiments, one or more reference measurements obtained shortlyafter the analyte sensor is manufactured, and before sensor use, areused for calibration. The reference measurement may be obtained in manyforms. For example, in certain embodiments, the reference measurementmay be determined from a ratio or correlation between the sensitivity ofa sensor (e.g., from a certain sensor lot) with respect to in vivoanalyte concentration measurements and the sensitivity of another sensor(e.g., from the same lot made in substantially the same way undersubstantially same conditions) with respect to in vitro analyteconcentration measurements at a certain time period. By providing acontinuous analyte sensor with a predetermined in vivo to in vitro ratioand a predetermined sensitivity profile (as described in more detailelsewhere herein), self-calibration of the sensor can be achieved inconjunction with high levels of sensor accuracy.

Determination of Sensor Sensitivity

In certain embodiments, self-calibration of an analyte sensor system canbe performed by determining sensor sensitivity based on a sensitivityprofile (and a measured or estimated baseline), so that the followingequation can be solved:

y=mx+b

wherein y represents the sensor signal (counts), x represents theestimated glucose concentration (mg/dL), m represents the sensorsensitivity to the analyte (counts/mg/dL), and b represents the baselinesignal (counts). From this equation, a transformation function can beformed, whereby a sensor signal is converted into an estimated glucoseconcentration.

It has been found that a sensor's sensitivity to analyte concentrationduring a sensor session will often change or drift as a function oftime. FIG. 1A illustrates this phenomenon and provides a plot of sensorsensitivities 110 of a group of continuous glucose sensors as a functionof time during a sensor session. FIG. 1B provides three plots oftransformation functions at three different time periods of a sensorsession. As shown in FIG. 1B, the three transformation functions havedifferent slopes, each of which correspond to a different sensorsensitivity. Accordingly, the differences in slopes over time illustratethat changes or drift in sensor sensitivity occur over a sensor session.

Referring back to the study associated with FIG. 1A, the sensors weremade in substantially the same way under substantially the sameconditions. The sensor sensitivities associated with the y-axis of theplot are expressed as a percentage of a substantially steady statesensitivity that was reached about three days after start of the sensorsession. In addition, these sensor sensitivities correspond tomeasurements obtained from standard reference glucometers (e.g., fromYellow Springs Instruments). As shown in the plot, the sensitivities(expressed as a percentage of a steady state sensitivity) of eachsensor, as measured, are very close to sensitivities of other sensors inthe group at any given time of the sensor session. While not wishing tobe bound by theory, it is believed that the observed upward trend insensitivity (over time), which is particularly pronounced in the earlypart of the sensor session, can be attributed to conditioning andhydration of sensing regions of the working electrode. It is alsobelieved that the glucose concentration of the fluid surrounding thecontinuous glucose sensor during startup of the sensor can also affectthe sensitivity drift.

With the sensors tested in this study, the change in sensor sensitivity(expressed as a percentage of a substantially steady state sensitivity),over a time defined by a sensor session, resembled a logarithmic growthcurve. It should be understood that other continuous analyte sensorsfabricated with different techniques, with different specifications(e.g., different membrane thickness or composition), or under differentmanufacturing conditions, may exhibit a different sensor sensitivityprofile (e.g., one associated with a linear function). Nonetheless, withimproved control over operating conditions of the sensor fabricationprocess, high levels of reproducibility have been achieved, such thatsensitivity profiles exhibited by individual sensors of a sensorpopulation (e.g., a sensor lot) are substantially similar and sometimesnearly identical.

It has been discovered that the change or drift in sensitivity over asensor session is not only substantially consistent among sensorsmanufactured in substantially the same way under substantially sameconditions, but also that modeling can be performed through mathematicalfunctions that can accurately estimate this change or drift. Asillustrated in FIG. 1A, an estimative algorithm function 120 can be usedto define the relationship between time during the sensor session andsensor sensitivity. The estimative algorithm function may be generatedby testing a sample set (comprising one or more sensors) from a sensorlot under in vivo and/or in vitro conditions. Alternatively, theestimative algorithm function may be generated by testing each sensorunder in vivo and/or in vitro conditions.

In some embodiments, a sensor may undergo an in vitro sensor sensitivitydrift test, in which the sensor is exposed to changing conditions (e.g.,step changes of glucose concentrations in a solution), and an in vitrosensitivity profile of the sensor is generated over a certain timeperiod. The time period of the test may substantially match an entiresensor session of a corresponding in vivo sensor, or it may encompass aportion of the sensor session (e.g., the first day, the first two days,or the first three days of the sensor session, etc.). It is contemplatedthat the above-described test may be performed on each individualsensor, or alternatively on one or more sample sensors of a sensor lot.From this test, an in vitro sensitivity profile may be created, fromwhich an in vivo sensitivity profile may be modeled and/or formed.

From the in vivo or in vitro testing, one or more data sets, eachincluding matched data pairs associating sensitivity with time, may begenerated and plotted. A sensitivity profile or curve can then be fittedto the matched data pairs. If the curve fit is determined to besatisfactory (e.g., if the standard deviation of the generated matcheddata pairs is less a certain threshold), then the sensor sensitivityprofile or curve may be judged to have passed a quality control andsuitable for release. From there, the sensor sensitivity profile can betransformed into an estimative algorithm function or alternatively intoa look-up table. The algorithm function or look-up table can be storedin a computer-readable memory, for example, and accessed by a computerprocessor.

The estimative algorithm function may be formed by applying curvefitting techniques that regressively fit a curve to matched data pairsby adjusting the function (e.g., by adjusting parameters of thefunction) until an optimal fit to the available matched data pairs isobtained. Simply put, a “curve” (e.g., a function sometimes referred toas a “model”) is fitted and generated that relates one data value to oneor more other data values and selecting parameters of the curve suchthat the curve estimates the relationship between the data values. Byway of example, selection of the parameters of the curve may involveselection of coefficients of a polynomial function. In some embodiments,the curve fitting process may involve evaluating how closely the curvedetermined in the curve fitting process estimates the relationshipbetween the data values, to determine the optimal fit. The term “curve,”as used herein, is a broad term, and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art (and is notto be limited to a special or customized meaning), and refers to afunction or a graph of a function, which can involve a rounded curve ora straight curve, e.g., a line.

The curve may be formed by any of a variety of curve fitting techniques,such as, for example, the linear least squares fitting method, thenon-linear least squares fitting method, the Nelder-Mead Simplex method,the Levenberg-Marquardt method, and variations thereof. In addition, thecurve may be fitted using any of a variety of functions, including, butnot limited to, a linear function (including a constant function),logarithmic function, quadratic function, cubic function, square rootfunction, power function, polynomial function, rational function,exponential function, sinusoidal function, and variations andcombinations thereof. For example, in some embodiments, the estimativealgorithm comprises a linear function component which is accorded afirst weight w1, a logarithmic function component which is accorded asecond weight w2, and an exponential function component which isaccorded a third weight w3. In further embodiments, the weightsassociated with each component can vary as a function of time and/orother parameters, but in alternative embodiment, one or more of theseweights are constant as a function of time.

In certain embodiments, the estimative algorithm function's correlation(e.g., R2 value), which is a measure of the quality of the fit of thecurve to the matched data pairs, with respect to data obtained from thesample sensors, may be one metric used to determine whether a functionis optimal. In certain embodiments, the estimative algorithm functionformed from the curve fitting analysis may be adjusted to account forother parameters, e.g., other parameters that may affect sensorsensitivity or provide additional information about sensor sensitivity.For example, the estimative algorithm function may be adjusted toaccount for the sensitivity of the sensor to hydrogen peroxide or otherchemical species.

Estimative algorithms formed and used to accurately estimate anindividual sensor's sensitivity, at any time during a sensor session,can be based on factory calibration and/or based on a single earlyreference measurement (e.g., using a single point blood glucosemonitor). In some embodiments, sensors across a population of continuousanalyte sensors manufactured in substantially the same way undersubstantially same conditions exhibit a substantially fixed in vivo toin vitro sensitivity relationship. For example, in one embodiment, thein vivo sensitivity of a sensor at a certain time after start of sensoruse (e.g., at t=about 5, 10, 15, 30, 60, 120, or 180 minutes aftersensor use) is consistently equal to a measured in vitro sensitivity ofthe sensor or of an equivalent sensor. From this relationship, aninitial value of in vivo sensitivity can be generated, from which analgorithmic function corresponding to the sensor sensitivity profile canbe formed. Put another way, from this initial value (which representsone point in the sensor sensitivity profile), the rest of the entiresensor sensitivity profile can be determined and plotted. The initialvalue of in vivo sensitivity can be associated with any portion of thesensor sensitivity profile. In certain embodiments, multiple initialvalues of in vivo sensitivities, which are time-spaced apart, and whichcorrespond to multiple in vitro sensitivities, can be calculated andcombined together to generate the sensor sensitivity profile.

In some embodiments, as illustrated in FIG. 2A, the initial value 210 ofin vivo sensitivity is associated with a time corresponding to the start(near the start) of the sensor session. As illustrated in FIG. 2B, basedon this initial value 210, the rest of the sensor sensitivity profile220 is plotted (e.g., plotted forward and backward across the x-axiscorresponding to time). However, as illustrated in FIG. 2C, in someembodiments, the initial value 210′ may be associated with any othertime of the sensor session. For example, as illustrated in FIG. 2C, inone embodiment, the initial value 210′ of in vivo sensitivity isassociated with a time (e.g., at about day 3) when the sensitivity hassubstantially reached steady state. From the initial value 210′, therest of the sensor sensitivity profile 220′ is plotted, as illustratedin FIG. 2D.

With other embodiments, although the in vivo to in vitro sensitivityrelationship was not equal, the relationship nonetheless involved aconsistently fixed relationship or function. By having a substantiallyfixed in vivo to in vitro sensitivity relationship, some of the sensorsdescribed herein can be factory calibrated by evaluating the in vitrosensitivity characteristic (e.g., one or more sensitivity valuesmeasured at certain time periods) of a sensor from a particular sensorlot at a manufacturing facility, defining the in vivo sensitivitycharacteristic of other sensors in the same sensor lot based on itsrelationship with the measured in vitro sensitivity characteristic, andstoring this calculated in vivo sensitivity characteristic ontoelectronics associated with the sensors (e.g., in computer memory of asensor electronics, discussed more elsewhere herein, configured to beoperably coupled to the sensor during sensor use).

Accordingly, with information obtained prior to the sensor sessionrelating to an in vivo to in vitro sensor sensitivity relationship and apredetermined sensor sensitivity profile, factory calibration isachieved in conjunction with high levels of sensor accuracy. Forexample, in some embodiments, the sensor was capable of achieving anaccuracy corresponding to a mean absolute relative difference of no morethan about 10% over a sensor session of at least about 3 days, andsometimes at least about 4, 5, 6, 7, or 10 days. In some embodiments,the sensor was capable of achieving an accuracy, over a over a sensorsession of at least about 3 days, corresponding to a mean absoluterelative difference of no more than about 7%, 5%, or 3%. With factorycalibration, the need for recalibration may be eliminated, or elserequired only in certain circumstances, such as in response to detectionof sensor failure.

With reference back to the study associated with FIG. 1A, the sensorswere built with a working electrode configured to measure aglucose+baseline signal and a corresponding auxiliary electrodeconfigured to measure only the baseline signal. Sensor electronics inthe sensor system subtracted the baseline signal from theglucose+baseline signal to obtain a signal associated entirely orsubstantially entirely to glucose concentration. In addition, analgorithmic function was generated and stored in sensor electronicsassociated with the sensors to estimate the sensitivity of these sensorsduring their lives. This algorithmic function is plotted in FIG. 1A andshown closely overlying the measured sensor sensitivities of thesensors. With the determination of baseline and sensitivity at any giventime during the life of a sensor, a transformation function is formed,whereby a sensor signal is converted into an estimated glucoseconcentration.

While individual sensors of a sensor group manufactured undersubstantially identical conditions have been found to generally exhibita substantially similar or a nearly identical sensor sensitivity profileand have a substantially similar or a nearly identical in vivo to invitro sensor sensitivity relationship, it has been found that at timesthe actual sensor sensitivity (e.g., sensitivity expressed as an actualsensitivity value, and not as a percentage of a substantially steadystate sensitivity) can vary between sensors. For example, even thoughindividual sensors may have been manufactured under substantiallyidentical conditions, they can have different sensitivitycharacteristics during use if they are exposed to different environmentconditions (e.g., exposure to radiation, extreme temperature, abnormaldehydration conditions, or any environment that can damage the enzyme inthe sensor membrane or other parts of the sensor, etc.) during the timeperiod between sensor fabrication and sensor use.

Accordingly, to compensate for potential effects resulting from theseconditions, in certain embodiments, the continuous analyte sensors areconfigured to request and accept one or more reference measurements(e.g., from a finger stick glucose measurement or from a calibrationsolution) at the start of the sensor session. For example, the requestfor one or more reference measurements can be made at about 15 minutes,30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activationof the sensor. In some embodiments, sensor electronics are configured toprocess and use the reference data to generate (or adjust) a sensorsensitivity profile in response to the input of one or more referencemeasurements into the sensor. For example, if a reference measurement ofglucose concentration is taken and input into the sensor at time=x, analgorithmic function of sensor sensitivity can be generated by matchingthe sensor sensitivity profile at time=x with the reference measurement.Use of the one of the one or more reference measurements at the start ofthe sensor in conjunction with a predetermined sensor sensitivityprofile permits self-calibration of the sensor without or with a reducedneed for further reference measurements.

FIG. 3 is a diagram illustrating different types of information that canbe input into the sensor system to define the sensor sensitivity profileover time, in one embodiment. Input information can include informationobtained prior to the sensor session 310 and information obtained duringthe sensor session 320. In the embodiment depicted in FIG. 3, bothinformation obtained prior to the sensor session 310 and informationobtained during the sensor session 320 are used to generate, adjust, orupdate a function 330 associated with the sensor sensitivity profile,but in another embodiment, the sensor system may be configured to useonly information obtained prior to the sensor session. In certainembodiments, formation of an initial sensor sensitivity profile canoccur prior to the sensor session, at the start of the sensor session,or shortly after the start of the sensor session. Additionally, incertain embodiments, the sensor sensitivity profile can be continuouslyadjusted, regenerated, or updated to account for parameters that mayaffect sensor sensitivity or provide additional information about sensorsensitivity during the sensor session. Information obtained prior to thesensor session can include, for example, the sensor sensitivity profilethat is generated before or at the start of the sensor session, aspreviously described. It can also include a sensitivity value associatedwith a substantially fixed in vivo to in vitro sensor sensitivityrelationship, as previously described.

Alternatively, instead of a fixed sensitivity value, the in vivo to invitro sensor sensitivity relationship may be defined as a function oftime between completion of sensor fabrication (or the time calibrationcheck was performed on sensors from the same lot) and the start of thesensor session. As shown in FIG. 4, it has been discovered that asensor's sensitivity to analyte concentration can change as a functionof time between completion of sensor fabrication and the start of thesensor session. FIG. 4 illustrates this phenomenon through a plot, whichresembles a downward trend in sensitivity over time between completionof sensor fabrication and the start of the sensor session. Similar tothe discovered change or drift in sensitivity over time of a sensorsession, this change or drift in sensitivity over time betweencompletion of sensor fabrication and the start of the sensor session isgenerally consistent among sensors that have not only been manufacturedin substantially the same way under substantially same conditions, butthat also have avoided exposure to certain conditions (e.g., exposure toradiation, extreme temperature, abnormal dehydration conditions, or anyenvironment that can damage the enzyme in the sensor membrane or otherparts of the sensor, etc.). Accordingly, the change or drift insensitivity over time between completion of sensor fabrication and thestart of the sensor session can also be modeled through a mathematicalfunction 420 that accurately estimates this change or drift. Theestimative algorithm function 420 may be any of a variety of functions,such as, for example, a linear function (including a constant function),logarithmic function, quadratic function, cubic function, square rootfunction, power function, polynomial function, rational function,exponential function, sinusoidal function, and combinations thereof.

Information obtained prior to the sensor session can also includeinformation relating to certain sensor characteristics or properties. Byway of example and not to be limiting, information obtained prior to thesensor session may include the particular materials used to fabricatethe sensor (e.g., materials used to form the sensor membrane), thethickness of the sensor membrane, the membrane's permeability to glucoseor other chemical species, the in vivo or in vitro sensor sensitivityprofile of another sensor made in substantially the same way undersubstantially same conditions, etc. In certain embodiments, informationobtained prior to the sensor session can include information relating tothe process conditions under which the sensor is fabricated. Thisinformation can include, for example, the temperature at which thesensor membrane was cured, the length of time the sensor was dipped in aparticular coating solution, etc. In other embodiments, informationobtained prior to the sensor session can relate to patient physiologicalinformation. For example, the patient's age, body mass index, gender,and/or historic patient sensitivity profiles, can be used as parametersto form the sensor sensitivity profile. Other information obtained priorto the sensor session that may also be used includes informationrelating to sensor insertion, such as, for example, location (e.g.,abdomen vs. back) or depth of sensor insertion.

In general, the sensor sensitivity functions can be created bytheoretical or empirical methods, or both, and stored as functions or aslook-up-tables, thereby allowing for sensor self-calibration thateliminates (or substantially reduces) the need for referencemeasurements. The sensor sensitivity functions can be generated at themanufacturing facility and shipped with the system or generated by thesystem shortly prior to (or during) use.

Calibration

An exemplary calibration process in accordance with some embodimentswill now be discussed with reference to FIG. 5. FIG. 5 illustrates anexample calibration process 500 that can use one or more of pre-implantinformation 502, internal diagnostic information 504 and externalreference information 506 as inputs to form or modify a transformationfunction 508. Transformation function 508 can be used to convert sensordata (e.g., in units of current or counts) into estimated analyte values510 (e.g., in units of analyte concentration). Informationrepresentative of the estimated analyte values can then outputted 512,such as displayed on a user display, transmitted to an external device(e.g., an insulin pump, PC computer, mobile computing device, etc.)and/or otherwise processed further. The analyte can be glucose, forexample.

In process 500, pre-implant information 502 can mean information thatwas generated prior to implantation of the sensor(s) presently beingcalibrated. Pre-implant information 502 can include any of the followingtypes of information:

-   -   A priori information including sensitivity values and ranges        from in vitro or in vivo testing; predetermined sensitivity        profile(s) associated with the currently used (e.g., implanted)        sensor, such a predicted profile of sensitivity change over time        of a sensor;    -   previously determined relationships between particular stimulus        signal output (e.g., output indicative of an impedance,        capacitance or other electrical or chemical property of the        sensor) to sensor sensitivity (e.g., determined from prior in        vivo and/or ex vivo studies) such as described in US Patent        Publication 2012-0265035, which is incorporated herein by        reference in its entirety;    -   previously determined relationships between particular stimulus        signal output (e.g., output indicative of an impedance,        capacitance or other electrical or chemical property of the        sensor) to sensor temperature (e.g., determined from prior in        vivo and/or ex vivo studies);    -   sensor data obtained from previously implanted analyte        concentration sensors, such as sensors of the same lot of the        sensor being calibrated and/or sensors from one or more        different lots;    -   calibration code(s) associated with a sensor being calibrated;    -   patient specific relationships between sensor and sensitivity,        baseline, drift, impedance, impedance/temperature relationship        (e.g., determined from prior studies of the patient or other        patients having common characteristics with the patient);    -   site of sensor implantation (abdomen, arm, etc.) specific        relationships (different sites may have different vascular        density);    -   time since sensor manufacture (e.g., time sensor on shelf, date        when sensor was manufactured and or shipped, time between when        the sensor was manufactured and/or shipped and when the sensor        is implanted); and    -   exposure of sensor to temperature, humidity, external factors,        on shelf.

In process 500, internal diagnostic information 504 can mean informationgenerated by the sensor system in which the implanted analyte sensor(the data of which is being calibrated) is being used. Internaldiagnostic information 504 can include any of the following types ofinformation:

-   -   stimulus signal output (e.g., the output of which can be        indicative of the sensor's impedance) of sensor using any of the        stimulus signal techniques described herein (the stimulus signal        output can be obtained and processed in real time);    -   sensor data measured by the implanted sensor indicative of an        analyte concentration (real-time data and/or previously        generated sensor data using the currently implanted sensor);    -   temperature measurements using the implanted sensor or an        auxiliary sensor (such as a thermistor) co-located with the        implanted analyte sensor or separately from the implanted        analyte sensor;    -   sensor data from multi-electrode sensors; for example, where one        electrode of the sensor is designed to determine a baseline        signal;    -   sensor data generated by redundant sensors, where one or more of        the redundant sensors is designed to be substantially the same        as at least some (e.g., have the same sensor membrane type), if        not all, of the other redundant sensors;    -   sensor data generated by one or more auxiliary sensors, where        the auxiliary sensor is having a different modality such (as        optical, thermal, capacitive, etc.) co-located with analyte        sensor or located apart from the analyte sensor;    -   time since sensor was implanted and/or connected (e.g.,        physically or electronically) to a sensor electronics of a        sensor system;    -   data representative of a pressure on sensor/sensor system        generated by, for example, a pressure sensor (e.g., to detect        compression artifact);    -   data generated by an accelerometer (e.g., indicative of        exercise/movement/activity of a host);    -   measure of moisture ingress (e.g., indicative of an integrity of        a moisture seal of the sensor system); and    -   a measure of noise in an analyte concentration signal (which can        be referred to as a residual between raw and filtered signals in        some embodiments).

In process 500, external reference information 506 can mean informationgenerated from sources while the implanted analyte sensor (the data ofwhich is being calibrated) is being used. External reference information506 can include any of the following types of information:

-   -   real-time and/or prior analyte concentration information        obtained from a reference monitor (e.g., an analyte        concentration value obtained from separate sensor, such as a        finger stick glucose meter);    -   type/brand of reference meter (different meters can have        different bias/precision);    -   information indicative of carbohydrates consumed by patient;    -   information from a medicament pen/pump, such as insulin on        board, insulin sensitivity, glucagon on board;    -   glucagon sensitivity information; and    -   information gathered from population based data (e.g., based on        data collected from sensors having similar characteristics, such        as sensors from the same lot).

Detection of Outliers

A beneficial feature of calibration is the ability to determine thepresence of outlier data points or outliers in the calibrationinformation (e.g., matched data pairs in the calibration set). As iseasily appreciated, the inclusion of poor data points, such as referenceglucose outliers, may influence any calibration in a negative fashion,e.g., create a bias or error in the calibrated sensor data derived fromerror from the calibration information. For example, errant fingersticks are an obvious source of poor reference data points. Errantfinger sticks originate from different sources including user error andsensor-BG mismatch.

Examples of user error include errant entry into receiver (e.g., 109mg/dl vs. 190 mg/dl) and poor finger stick technique (e.g., “sugar onthe finger”). Examples of sensor-BG mismatch include time lag and thesensor not tracking (e.g., BG trending up, sensor trending down).

Errant calibrations may result in a poorly drawn calibration line, andresulting error in the transformation function used to convert thesensor-generated data into clinical units. Consequently, the ability toidentify and/or remove or reduce the presence of outliers ensures thatthe calibration set carried forward is more accurate, thus yielding amore accurate calibration line.

FIG. 6 is a flowchart 600 illustrating a process for dynamically andintelligently determining or detecting outlier data points in accordancewith an embodiment of the disclosure. External BG reference data (e.g.,blood glucose values from finger stick checks) may or may not be usefulin calibration, depending on the amount error associated therewithand/or the reason for any error or discrepancy between the BG referencedata and time corresponding sensor data. As explained above, detectingoutlier data points is highly desirable as the exclusion of such datapoints may minimize and/or prevent error in sensor calibration, makingcalibration more reliable.

At block 610, a processor module may be configured to iterativelyevaluate a plurality of subsets of data in the calibration data set. Insome embodiments, the processor module determines the best subset ofdata from the calibration data set. In general, the iterative evaluationincludes iteratively evaluating different calibration data from a singlecalibration set during a particular calibration or iterativelyevaluating different calibration data from a collective calibration setat different calibrations, whereby trends in error may be identified.

In some embodiments, a subset can be at least 1/N of the matched datapairs, or be between 0.5*N and N−1, where N is the number of matcheddata pairs in a full set. In some embodiments a subset can be at least ½of the matched data pairs. In some embodiments, the calibration data setwill include at least 5 matched data pairs; however, some calibrationdata sets will include 10 or more matched data pairs. As used herein,the data points in the calibration data set may include matched datapairs, which include a reference data value (e.g., BG value) and asensor data value (e.g., counts).

In some embodiments, it may be advantageous to reduce the number ofsubsets that that are evaluated in order to improve computationalefficiency. For example, heuristic methodologies may be applied by usingprior information (e.g., information from previous outlier detectionevaluations) to better identify best data pairs (or subsets) and/orworst data pairs (or subsets). In other words, trends or historyassociated with each match data pair or subset may be used to limit theiterative evaluation of subsets. Advantageously, the heuristic approachto determining which subsets to evaluate takes advantage of the factthat the matched pairs in the subset used in the previous iterationsrepresent points that are close to the regression line; this is expectedto yield good results in spite of the reduction in the number of subsetsexamined. One heuristic methodology includes “pre-screening” the newestBG value by comparing a newly formed matched data pair to the bestsubset determined from the previous iteration of outlier detection; thismay allow an immediate decision to be made prior to full iterativeprocessing.

In some embodiments, matched data pairs may be assigned weights based ona likelihood of being an outlier. The processor module may use anysubset and compute the residuals, after which each point may be assigneda weight based on how far the point is from the line (e.g., theresidual). These weights may be combined in a way to signify how likelythis point is to be an outlier. For example, the lower the weight is,the better the point fits with the rest of the matched pairs or viceversa. For example, the weights can be the average of the squares of theresiduals obtained from all the subsets on which it was evaluated. Insome embodiments, the processor module may use the only the subsets withthe lowest weights (e.g., below a threshold), effectively choosing thepoints that are best correlated with most other points.

In some embodiments, each subset can be used to draw a line (e.g., usingregression analysis). In some embodiments, a plurality of linesassociated with the data sets is generated to determine the best subset.For example, generating a plurality of lines may result in a best lineassociated with the best subset. In some embodiments, the best linecomprises a line that has the best fit for all points used to generatethe line. The best fit may be determined using a one or more functionsselected from the following list: For example, regression techniquesuseful for determining statistical associations of data sets, such as,linear regression (e.g., Least Quantile of Squares (“LQS”), LeastTrimmed of Squares (“LTS”)), non-linear regression, rank correlation,least mean square fit, mean absolute deviation, and mean absoluterelative difference, or the like may be used.

In some embodiments, each subset defines a convex hull or convexenvelope. In some embodiments, a plurality of convex envelopesassociated with the data sets is generated to determine the best subset.For example, generating a plurality of convex envelopes may result in abest convex envelope associated with the best subset. In someembodiments, the best convex envelope comprises a convex envelope thathas the best fit for all points used to generate the convex envelope.The best fit may be determined using some standard algorithm for convexhull computation such as one or more functions selected from thefollowing list: convex hull algorithms such as Graham scan (e.g., O(Nlog N) complexity), QuickHull (e.g., O(N log N) average complexity), andMonotone chain (e.g., O(N log N)) and/or rotating calipers algorithm forcomputing the diameter and width of a convex hull (e.g., (O(N) time oncethe convex hull is computed).

In some embodiments, using a convex hull according to a first heuristicto reduce the number of subsets is desirable because any outliers oroutlier data points will be on the convex hull (e.g., be part of theline defining the convex hull). Thus when considering the subsets, theconvex hull (which takes N log N times) can be constructed, and onlysubsets that are completely inside the convex hull may be used to draw acalibration line. Once a calibration line is drawn, points on the hullmay be included one by one into the subset, each time using the pointclosest to the current line. If, after including a sufficient number ofpoints, any of the remaining points on the convex hull are found to befar from the line, such points can be singled out as outliers.

At block 620, the processor module may be configured to identify aboundary or confidence interval associated with a best subset. In someembodiments, the boundary or confidence interval may be the boundarylines of the best convex envelope.

In some embodiments, the boundary or confidence interval of step may bean acceptable deviation or scatter from the best line. For example, eachpoint used to generate the best line may be compared against the bestline to determine the residual value (e.g., the most accurate pointsrelative to the best line) for each point. In some embodiments, theboundary or confidence interval is a predetermined multiple of thescale. The scale is herein defined as the average value of the residualsmultiplied by some consistency factor. The boundary interval may then bedefined as the average residual multiplied by a consistency factor(e.g., multiplied by a factor like 2). In other embodiments, theboundary or confidence interval is a set value, e.g., may be preset aspart of factory settings. In some embodiments, the boundary orconfidence interval is calculated and updated in real-time. Thereafter,the residual value for each point may be compared against the boundaryor confidence interval to determine if the residual value for each pointfalls within the boundary or confidence interval.

In some embodiments, the diameter and width of the convex hull may becomputed using a second heuristic. As used herein, the diameter is themaximum distance between two points in the convex hull and the width isthe minimum distance between two parallel lines of support. Generally,the points in a set will cluster around a line if the diameter is muchlarger than the width. In some embodiments, the ratio of the area of theconvex hull to the diameter may be compared. In some embodiments,walking through the points on the convex hull and finding the residualswith respect to the diameter, may identify any point outside a givenresidual as an outlier.

At block 630, the processor module may be configured to identify allvalues (e.g., data points) outside the boundary or confidence intervalas possible outliers. For example, if any residual value falls outsidethe boundary or confidence interval, then each point associated with theresidual value that falls outside the boundary or confidence intervalmay be identified as a possible outlier.

A few examples of methods for performing blocks 610 through 630 areprovided, below:

Example 1 Least Quantile of Squares (“LQS”)

In one exemplary embodiment, the Least Quantile of Squares (“LQS”) orLeast Median of Squares (“LMS”) robust regression technique allows fordiscrimination of contaminated data from 0% to 50%, meaning outliers maybe detected if up to 50% of the data is contaminated. For example, thehighest breakdown value LQS may have is 50% because it is at this pointthat e.g., the good data becomes indiscernible from bad data. LQS may bethought of as a sampling algorithm (e.g., it tries all permutations in acalibration data set to draw lines). Ultimately, LQS operates byminimizing the residual around a desired quantile.

Example 2 Least Trimmed of Squares (“LTS”)

In another exemplary embodiment, the Least Trimmed of Squares (“LTS”) isa robust statistical method that fits a function to a set of datawithout being impacted by outliers. In some exemplary embodiments, amodified version, such as the FAST-LTS algorithm may be used. Thefunction first calculates all possible line combinations of based onsubsets of the calibration set (e.g., if the calibration set has 6points then one of the lines may be drawn using points 1, 3, 4, 6) asshown below:

${\# \mspace{14mu} {of}{\mspace{11mu} \;}{Possible}\mspace{14mu} {Combos}} = \frac{N!}{{( {N - h} )!} \cdot {h!}}$

If there are six points in the calibration set, then the # of linesdrawn will be 15. In some exemplary embodiments, the FAST_LTS algorithmbegins with all 2-point combinations and then adjusts the line in a wayto encompass h points. In some embodiments, the FAST_LTS algorithm maystart with all h-point combinations. In some embodiments, where thecalibration data is sparse, there is no advantage in terms ofcomputational efficiency between starting with 2 points or h points.Note that h can be any number as long as it satisfies 0.5*N<h<N−1.

In some embodiments, in order for the function to run, the calibrationset must have 5 or more points; however, the input to the function couldbe a subset of the calibration set (e.g. all points in the last 48 hoursif there are at least 5 or more, or the last 5 points entered into thedevice in the event the sensor goes out of calibration). Once allh-point combinations are calculated, the function loops through andcalculates an Ordinary Least Squares (“OLS”) line with each h-pointcombination. In other words, even though there are 6 points in thecalibration set, only 4 of those are used to calculate the line. Anexample of a 6 point calibration set (FIG. 7) and one of the OLS linesdrawn (FIG. 8) using h-points are provided.

Still referring to FIG. 8, each line can be bounded by one or moreacceptable boundaries for slope and baseline that may be static ordynamic. In some embodiments, the boundaries may include confidenceintervals. In some embodiments, the acceptable boundaries include anupper and lower boundary defined by minimum and maximum slopes andbaselines, against which certain calibration information (e.g.,calibration line and/or matched pairs) are compared.

An estimate of the expected signal may be calculated using all the BGvalues in the calibration set and a regression line drawn. The residualsin the signal (raw data points) may be calculated, and then the absolutevalue of each of the residuals may be taken and then sorted fromsmallest to largest. When using a wedge parameter, if there is no linethat fits within the wedge parameter, the function exits because thereare no realistic lines being drawn. In some embodiments, informationrelated to a failure to pass the wedge parameters may be used to drive achange in the limits used in the processor module or the wedgeparameters or to communicate with algorithms of the system (e.g.calibration algorithms) to suggest that some points in the calibrationset need to be dropped.

In some embodiments, once the optimal line is selected, the residualsmay be calculated, squared and then sorted from smallest to largest,thereby enabling an estimate of the scale (dispersion around the line)to be calculated. The scale value is greater than 0, but it could bebounded by another value greater than 0 (e.g. a low limit, a high limit,or both) to avoid further calculations seen below. For example, if thehigh limit of the scale is consistently getting hit (e.g. scale>highlimit), such information may be communicated with other algorithms ofthe system (e.g. calibration) because such information may be indicativeof too much scatter in the calibration set, that the calibration set isnot linear enough and/or that one or more data pairs should be removed.Having calculated the scale, an estimate of possible outliers (incounts) can be made. All points whose ratio of residual to scale exceedsa threshold (e.g. residual_(i)/scale>2.25) may be flagged as an outlier.One or more levels of scales (e.g., preliminary, intermediate and finalscales) may be calculated and processed as described above. Otherdiagnostics may be applied to the scale(s) to improve accuracy as isappreciated by one skilled in the art.

In some embodiments, the possible outliers may be removed from thecalibration set immediately. In some embodiments, the possible outliersmay be removed from the calibration set retrospectively at somecalibration after the calibration that resulted in the flagging. In someother embodiments, the flagging of possible outliers may signal thealgorithm to check for clinical relevancy of the error before removingthe point(s) from the calibration set. In some other embodiments, onlythe relevancy of the error may be used to remove points from thecalibration set.

Referring now to block 640, the processor module may be configured toevaluate the relevancy of possible outliers identified at block 630 todetermine a clinical relevancy of, to discriminate the root cause of theerror in the possible outlier, trends of outlier information, or thelike, resulting in outlier information useful for further processing(block 650). Although some embodiments for flagging potential outlierpoints are described above (blocks 610-630), relevancy evaluation (640)and responsive process (650) may be combined or applied with anymethodology for determining outlier information. While not wishing to bebound by any particular theory, errors that cause the flagging ofpossible outliers may be caused by a variety of different situations.For example, a phenomenon that may be described as “dip and recover,”which occurs during early implantation of a subcutaneous sensor, may bedetermined. As another example, physiological changes in thecompartments of the test samples (e.g., capillary blood vs. interstitialfluid) may be inherently different at certain times. Additionally, theerror may be flagged as an outlier based on the statistical analysis orresiduals as evaluated in raw counts, but the error may not beclinically relevant, for example, the error in the blood glucosereference value compared regression line (best line or calibration line)captures clinical error.

In some embodiments, the processor module may evaluate the possibleoutliers only when the most recent BG is determined to be a possibleoutlier. In some embodiments, the relevancy determination includesexamining one or more factors from the following list, including but notlimited to: time since sensor implant, trends in outlier evaluation, theamplitude of error of a data point relative to the (best) line (e.g.,evaluated in clinically relevant units such as mg/dL or mmol/L), thedirection of error a data point relative to the (best) line (e.g., thedirection of the error is less than 0 or greater than 0 when BGreference value is reading higher than sensor value or lower than sensorvalue, respectively, which may be evaluated in clinically relevantunits), a clinical risk of the data (e.g., static or dynamic risk) atthe time stamp of the matched data pair, a rate of change of the analyteconcentration or derivative of the sensor data associated with thematched data pair, a rate of acceleration (or deceleration) of theanalyte concentration or second derivative of the sensor data associatedwith the matched data pair, or the like. In some embodiments, therelevancy determination evaluates an influence of the possible outlieron the regression line (best line or calibration line). For example,sometimes in regression analysis, a data point has a disproportionateeffect on the slope of the regression equation. One way to test theinfluence of an outlier is to compute the regression equation with andwithout the outlier; a threshold may be applied, such as greater than 25pA/mg/dL, to determine whether the influence is disproportionate.

In some embodiments, the check for relevancy may be performed because ahighly correlated calibration set may flag a point as a statisticaloutlier but the blood glucose may be, e.g., only 5 mg/dl off the line,which is well within the error of standard blood glucose meters. Forexample, a clinical relevance may be calculated based on an estimatedglucose value (“EGV”), which also may referred to as calibrated glucosevalue, as shown below:

${E\; G\; V} = \frac{( {{Counts}_{i} - b} )}{m}$

The EGV may then be checked against the matched BG. In some embodiments,if the matched BG is less than or equal to a predetermined threshold,such as ≦75 mg/dl, then the error check utilizes a first criterion, suchas an absolute difference and if the matched BG is greater than apredetermined threshold, such as >75 mg/dL, then the error checkutilizes a second criterion (different from the first), such as anabsolute relative difference (“ARD”). In one exemplary embodiment, ifthe error check is an absolute difference (BG≦75 mg/dl), then thethreshold for the error is 20 mg/dl (˜3 sigma of BG error threshold) andif the error check is an ARD (BG>75 mg/dl) then the error threshold is25% (˜3 sigma of BG error threshold). Other thresholds and metrics knownto those of skill in the art may be applied. As a result of the clinicalrelevancy determination, it may be determined that the error associatedwith the possible outlier is not clinically relevant and the matcheddata pair may remain in the calibration set. For example, if thecalculated error is within predetermined thresholds, then the pointinitially identified as a possible outlier (e.g., using a statisticalanalysis of error in the y-direction) may be re-identified as clean.

However, if the result of the relevancy determination test is adetermination that the error is relevant, then processing may move toblock 650. For example, the relevancy determination test may compare aparameter associated with the error described above against aquantitative outlier criterion (e.g., threshold), wherein errors thatmeet the criterion for clinically relevant outliers (e.g., outside apredetermined threshold) are flagged in outlier information.

In some embodiments, the relevancy determination test evaluatesadditional criteria, such as time since implant, since time sinceimplant may be indicative of phenomena known to occur during the life ofthe sensor. For example, transcutaneous and implantable sensors areaffected by the in vivo properties and physiological responses insurrounding tissues. For example, a reduction in sensor accuracyfollowing implantation of the sensor is one common phenomenon commonlyobserved. This phenomenon is sometimes referred to as a “dip andrecover” process. Although not wishing to be bound by theory, it isbelieved that dip and recover is triggered by trauma from insertion ofthe implantable sensor, and possibly from irritation of the nerve bundlenear the implantation area, resulting in the nerve bundle reducing bloodflow to the implantation area. Alternatively or additionally, dip andrecover may be related to damage to nearby blood vessels, resulting in avasospastic event. Any local cessation of blood flow in the implantationarea for a period of time leads to a reduced amount of glucose in thearea of the sensor. During this time, the sensor has a reducedsensitivity and is unable to accurately track glucose. Thus, dip andrecover manifests as a suppressed glucose signal. The suppressed signalfrom dip and recover often appears within the first day afterimplantation of the signal, most commonly within the first 12 hoursafter implantation. It is believed that dip and recover normallyresolves within 6-8 hours. Identification of dip and recover can provideinformation to a patient, physician, or other user that the sensor isonly temporarily affected by a short-term physiological response, andthat there is no need to remove the implant as normal function willlikely return within hours. In one example, a combination of suppressedsignal (e.g., detection of downward shift in sensor sensitivity) duringa predetermined time period (e.g., during the first 36 hours afterimplantation) may be used to identify the root cause of the outlier asthe dip and recover phenomenon.

Other physiological responses to the implantable sensor can also affectperformance of the implantable sensor. For example, during wound healingand foreign body response, the surface of the implantable sensor canbecome coated in protein or other biological material to such an extentthat the sensor is unable to accurately track blood glucose. Thisphenomenon is sometimes called “biofouling” and biofouling oftenmanifests itself as a downward shift in sensor sensitivity over time.Similarly, the implantable sensor can become encapsulated by biologicalmaterial to such an extent that the sensor is unable to provide glucosedata, and the sensor is considered to effectively be at end of life. Insome cases, the implantable device can be programmed to correct forerrors associated with biofouling and end of life, so thatidentification of these phenomenon aids in providing more accurateglucose data. In one example, a combination of downward rate of changeof sensor sensitivity over a predetermined time period (e.g., at least 5days after sensor implantation) may be used to identify the root causeof the outlier as related to bio-fouling or end of life.

Another physiological affect that has been observed may be referred toas a “compartmental effect.” This effect results from differences inactual physiological glucose levels in different compartments in thebody, for example in capillary blood as compared to interstitial fluid.Identification of these physiological phenomena also generally providesinformation useful in determining whether the error associated with theflagged outlier is related to the reference BG value or the sensor data,and may be processed responsive thereto. Identification may includeadditional input, such as time since implant and/or timing, locationand/or amount of insulin injected. In one example, when the host's rateof change of glucose exceeds a threshold and/or a meal has been recentlyconsumed, and when outlier does not follow a trend of similar errors,the processor module may identify the root cause as a transientcompartmental effect.

Other factors that may be evaluated to identify the root cause of theoutlier may include secondary (e.g., redundant) sensors, trendinformation associated with outliers (outlier information) from previousiterations of outlier detection, or the like. Trend information may beparticularly useful where a data point (matched pair) has beenconsistently showing a particular error mode, perhaps not withsufficiency to meet the outlier criteria of the evaluation block 630,but perhaps with enough sufficiency based on trend criteria (exceeding athreshold for x consecutive outlier evaluations, such as 2, 3, 4, 5, 6,7 or 8). In general, any number of conditions and/or criteria may beevaluated to determine the relevancy (e.g., root cause and/or clinicalrelevancy) of the possible outlier.

At block 650, the processor module may be configured to process dataresponsive to outlier information determined at block 640. In general,matched data pairs flagged as outliers (outlier information) after therelevance determination test (block 640) may then go through additionalprocessing. The outlier may be removed temporarily or permanently fromthe calibration set, prospectively or retrospectively. An outlier may beprocessed accordingly to block 650 with or without the relevancedetermination check of block 640 and/or may be iteratively processed ateach outlier detection check. Additionally or alternatively, other data,such as information transmitted to an insulin delivery device, promptson the user interface, or other related processing may be includedherein.

In one example, when a series of iterations of outlier detectionindicate a trend of sensor error, the processor module may increase thenumber of reference data requested and/or relied up on for calibration,for example when bio-fouling and/or sensor end of life is detected, asdescribed in more detail elsewhere herein. In another example, when aseries of iterations of outlier detection indicate a trend of referenceblood glucose error, the processor module may decrease the number ofreference data and/or not rely on reference data for sensor calibration,for example, when the sensor has stabilized and shows no signs of driftof sensor sensitivity. Such processing may allow the continuous glucosesensor system to switch between more or less (or no) reliance on bloodglucose reference measurements based on the measure of accuracy of thesensor data versus the reference blood glucose measurements over time.

For example, the processor module may flag an outlier, but keep theflagged outlier in the calibration data set until the next data point iscollected. In such an example, the flagging of the outlier enables aniterative process to confirm (or deny) the outlier based on additionaldata, such as the next reference data point and resulting matched datapair, to avoid false positive identification of outliers and resultingconsequences to the user experience. In other words, when an outlier isflagged, the root cause of the outlier could be related to either thesensor data point or the reference data point (BG), since the flaggedoutlier would be influenced by both the sensor and the reference input.In some embodiments, when the next reference data point is input (aftera flagged outlier), its respective matched data pair may confirm thatthe sensor performance is changing and the flagged outlier(s) is/areactually more reliable that the other matched data pairs in thecalibration set, resulting in the removal of some or all other datapairs in the calibration set except the two flagged outliers and/or mostrecent matched data pairs. On the other hand, the next matched data pair(after flagging of an outlier) may not be identified as an outlier, andthereby confirm that the previously flagged outlier should be removedfrom the calibration set. Criteria for evaluating the next matched datapair with respect to a previously flagged outlier may include aniterative process as described herein with respect to dynamically andintelligently determining or detecting outlier data points (600) orusing other criteria and analysis specific to determining the root causeand/or subsequent interpretation of the flagged outlier.

In one embodiment, if the most recent BG reference value is identifiedas a possible outlier (block 630), then the direction of the error ischecked using a clinical relevancy test (block 640). For example, if thedirection of the error is less than 0 (e.g., BG reference value isreading higher than sensor value), then at block 650, all points flaggedas outliers, except for the most recent BG reference values, are removedfrom the calibration set. On the other hand, if the most recent BGreference value is identified as a possible outlier, and the directionof the error is checked using the clinical relevancy test is positive(e.g., sensor value higher than BG reference value), then no points areremoved from the calibration set. Other methods for inclusion and/orexclusion of matched data pairs may be used to complement and/orsupplement outlier detection methods described in flow chart 600.

This embodiment utilizes a clinical relevancy test that takes intoaccount the direction of the error associated with a particular BGreference value (e.g., the matched data pair that includes the BG),whereby when the most recent BG reads higher than the sensor value, thenit is assumed that the sensor is tracking well and the BG iscontaminated due to user error, e.g., “sugar on the finger,” and wherebywhen the sensor value reads higher than the BG value by a threshold, itis assumed that the BG was flagged as an outlier because of drift (e.g.,change in sensitivity of the sensor over time). In such embodiments, thecalibration set may be culled to remove poor data points (e.g., criteriathat ensures at least about ½, ⅔ or ¾ of the matched data pairs in thecalibration data set are removed). With a clean calibration set, thecalibration line may be drawn using one or more preferred functions. Ifthe system goes out of calibration, a modified process of outlierdetection may be performed. As used herein, “out of calibration” refersto a state where sensor data is not converted due to lack of confidencein the calibration information. An exemplary modified outlier detectionusing a calibration set that does not include the BG value that causedthe system to fall “out of calibration,” may evaluate the following: A)if the most recent BG (BG1) is flagged as an outlier and BG1 readshigher than the sensor, then the calibration set (CAL1) before BG1 wasentered is stored and the system waits for the next BG (BG2) to beentered. Once BG2 is entered, it is passed into outlier detection withCALL If BG2 is flagged as an outlier, then CAL1 is cleared and BG1+BG2is made the new calibration set (CAL2). If BG2 is clean, BG1 is thrownout and CAL1+BG2 becomes the new calibration set (CAL2′) and B) BG1 isreading lower than the sensor, then the calibration set is cleared andBG1 is stored the system waits for a new BG to start a new calibrationset.

In some embodiments, information associated with the flagged outlier maybe transferred to another algorithm of the system and/or used to triggeranother algorithm of the system. For example, an outlier may beparticularly identified as being indicative of a downward sensitivityshift meeting a criterion (e.g., based on an analysis of the errorbetween the BG and corresponding EGV of the matched pair), suchinformation may be a risk factor indicative of end of life and may beused as an input into the end of life determination function and/or mayinitiate the end of life algorithm. As another example, an outlier maybe particularly identified as being associated with a “dip and recover”event based on time since implant, the host's history with continuousglucose sensing and/or the like. Accordingly, this information may beuseful by the processor module to trigger a suspend of the glucose datafor a predetermined time period (e.g., “Dip and recover” known to have afinite time period) and/or to inform the user on the user interface thatcertain implantation effect may cause some inaccuracies for the next xnumber of minutes or y number of hours (e.g., 2, 4, 6, 8, 10, 12, 16 or24 hours).

In some embodiments, interaction of the host with the device may beuseful input, which together with the knowledge of the flagged outlier,may be indicative of a user's frustration with the device and/or thehost may require additional guidance. In some embodiments, when the hostis regularly entering BG values (e.g., outside of a normal calibrationscheme) at a rate that is more than periodically (e.g., more than X BGentries in the last Y minutes, for example more the 2, 4, or 6 entriesin the last 15, 30 or 60 minutes), then it may be an indication that thehost is noticing an error and may be trying to correct the sensor by“feeding” BG values to the device. The resulting processing may includetrusting the BG values and providing feedback in the form of messagesand/or glucose values that shows the host that the device is receivingand responding to the BG inputs. One example may include asking the hostquestions through prompts on a user interface to determine the rootcause. Another example may include considering trusting the user's inputmore than the outlier detection test and thereby “unflagging” theoutlier temporarily or permanently so that the so-called flagged outlierwill actually influence the estimated glucose values displayed to auser, temporarily or permanently, so as to appear to more closely alignwith his/her BG reference values.

If the most recent BG is not flagged as an outlier, it can be assumedoutlier detection ran successfully and the outlier detection line(optimal OLS line) is within the wedge of the line logic function (theline drawing logic and outlier detection may have different wedges),then the system is in calibration and uses the line drawn by outlierdetection.

In some embodiments, the processing responsive to outlier detectiondetermines whether and/or how much pre-implant information 502, internaldiagnostic information 504 and/or external reference information 506 maybe relied upon for any given calibration. In one example, wherein a lackof confidence in reference glucose data resulting from iterativefailures of matched data pairs to pass one or more evaluations of theprocesses of flow chart 600 (for previous calibrations) exists, theprocessor module may determine that calibration should rely solelypre-implant information 502 and/or internal diagnostic information 504.In some embodiments, the use of pre-implant information 502, internaldiagnostic information 504 and/or external reference information 506 maybe selected and/or selectively weighed based on the outlier detectionprocess and/or other diagnostic information, such as internal diagnosticinformation. In some embodiments, depending on other parameters, such astime since implant, or historical sensor performance for a particularpatient, the use of external reference information may be adaptivelydetermined and applied to calibration. In one example, a sensor sessionmay begin (initially calibrate) by relying solely on pre-implantinformation 502 (e.g., a priori sensitivity information), andsubsequently rely more or less on the pre-implant information 502 as thesensor session progresses and additional internal diagnostic information504 and/or external reference information 506 is obtained. In anotherexample, the sensor session may rely at least partially on externalreference information 506 during a sensor session, but adaptively relyless and less on the external reference information as the sensorsession progresses and increased confidence in the pre-implantcalibration information and/or internal diagnostic information isdetermined as the sensor session progresses. As another example, whenpre-implant information in the form of patient historical information isavailable (i.e., because the patient has worn the sensor previously),the sensor may rely more on the pre-implant information duringsubsequent sensor sessions as compared to the first one or few sensorsessions. Other combinations and selective uses of pre-implantinformation 502, internal diagnostic information 504 and/or externalreference information 506 for calibration of the sensor (i.e.,adaptively and/or progressively over a sensor session) may be envisionedas well.

Referring back to flow chart 600, in one exemplary embodiment, iterativeevaluations may be performed on an entire calibration set, for exampleusing linear regression, rather than on a subset of the calibration set.Stated another way, either iteratively evaluating a plurality of subsetson a single calibration set during a particular calibration oriteratively evaluating the entire calibration set at differentcalibrations (which could also be considered subsets of all data pairscollected during a sensor session), the end result may be the same(e.g., looking for trends in error to confirm the clinical relevancy ofthe error). For example, traditional linear regression analysis (e.g.,ordinary least squares (“OLS”)) homoscedasticity is assumed. In otherwords, the standard deviations of the error terms are constant andindependent of the x-value (ε˜N(0, σ)). OLS may identify outliersthrough repetition of error.

It should be appreciated that block 610 covers the iterative evaluationof the certain matched data pairs at different calibrations. Block 620recites: identify a boundary or confidence interval associated with abest subset. In some embodiments, based on the regression line (e.g.,drawn from the calibration set or a subset of the calibration set), itmay be possible and desirable to track which data pair(s) isconsistently showing error above a threshold (e.g., trended with eachcalibration evaluation). For example, while there may not be enougherror to know whether a data pair should be removed using a singleevaluation, when looking at a trend of multiple calibration evaluations(e.g., iteratively over a day or two or more), the data pair(s) may showa consistent trend of error. The boundaries or confidence interval usedto watch the trending of these may be the same or different from (e.g.,less stringent than) the boundaries or confidence intervals associatedwith other aspects, such as the flagging of possible outliers. Forexample, at initialization, the system may start counting error andlooking for frequency of error over time. Error may be calculated byusing the slope (M) and intercept/baseline (B) to recalculate the EGVfor each of the count values of each data pair in the calibration set.This may provide a normalized comparison for each data pair in thecalibration set. With the BGs in the calibration set, the error in theEGV calculated may be determined, as shown below.

${Error}_{i} = {100^{*}\frac{{{EGV}_{i} - {BG}_{i}}}{{BG}_{i}}}$

This equation advantageously allows the error to be determined in unitsof blood glucose (and thus a better clinical relevancy of the error) foreach data pair. In some embodiments, the relevancy may be determinedbased on a trending of error (e.g., data pair identified as possibleoutlier 5 out of the last 6 calibrations). In other words, bothfrequency of the error, and the fact that the error is being evaluatedafter conversion of the counts (for each data pair) into units of bloodglucose, may be used to evaluate the relevancy (e.g., clinicalrelevancy) of the possible outliers. In this embodiment, when a datapair has been tracked with a clinical error (e.g., error in units ofblood glucose) greater than a certain threshold for at least x of thelast y calibrations/evaluations, it may be affecting the calibrationerror in a clinically relevant manner. In some embodiments, this“trending” error may be flagged e.g., as an outlier. In someembodiments, outliers or points identified to have some moderate levelof error may be flagged). For example, this embodiment qualitativelysays when a certain error has been seen frequently (x number of times inthe past y amount of time) with the same data pair, the data pair isremoved from the calibration set and a new calibration line (e.g., dummyversion) is tested to determine if the new calibration line would passother tests for accuracy in calibration (e.g., wedge parameters). If thenew calibration line passes the tests or is as good as the last or isbetter than the last (e.g., based on predetermined criteria), then theremoved data pair is permanently removed from the calibration set (e.g.,culled or thrown out).

Detection of End of Life

Embodiments of glucose sensors described herein may have a useful lifein which a sensor can provide reliable sensor data. After the usefullife, the sensor may no longer be reliable, providing inaccurate sensordata. To prevent use beyond the useful life, some embodiments notify auser to change the sensor after it has been determined that the sensorshould no longer be used. Various methods can be used to determinewhether a sensor should no longer be used, such as a predeterminedamount of time transpiring since the sensor was first used (e.g., whenfirst implanted into a user or when first electrically connected tosensor electronics) or a determination that the sensor is defective(e.g., due to membrane rupture, unstable sensitivity or the like). Onceit is determined that the sensor should no longer be used, the sensorsystem can notify a user that a new sensor should be used by audiblyand/or visually prompting a user to use a new sensor and/or shuttingdown a display or ceasing to display new (or real-time) sensor data onthe display, for example.

In some embodiments, continuous glucose monitors may show signs ofsensor “end of life” near their end of life. The signs of end of lifemay be recognized and total sensor end of life and any resulting usersafety or inconvenience may be prevented. In some embodiments, thisdisclosure describes distinct sensor failure signatures and how they canbe reliably recognized and detected.

Referring to FIG. 9, a flowchart 700 illustrating a process fordynamically and intelligently determining or detecting the end of lifeof a sensor in accordance with an embodiment of the disclosure is shown.As explained above, detecting the end of life of a sensor is highlydesirable as the accurate reading of a user's blood glucose level isimportant in diabetic monitoring.

At block 710, the processor module may be configured to evaluate aplurality of risk factors that may be indicative of sensor end of life,for example using an end of life (“EOL”) risk factor instruction(s),algorithm(s) and/or function(s). In general, the processor module mayinclude one or more functions that evaluate a plurality of individualrisk factors that may each partially provide an indication of the end ofsensor life. In general EOL symptoms are progressive, e.g., not allsymptoms (or episodes) indicate sensor failure. Each of the risk factorsmay be evaluated periodically or intermittently as often as with thereceipt of sensor data (e.g., every 5 minutes) or more intermittently(e.g., every few hours or every day). The risk factors can beiteratively determined, averaged or trended over time and the resultsused in later processing. In some embodiments, the evaluation of one ormore risk factors may be triggered by another event, such as a trendederror in BG (e.g., from outlier detection) meeting one or more criteria.

In some embodiments, certain risks factors are evaluated more often orless often than the other of the risk factors. In some embodiments, whenone or more risk factors meet one or more predetermined criteria, any ofa) the process for dynamically and intelligently determining ordetecting the end of life of a sensor (700), b) the process (710) ofevaluating one or more other risk factors or c) the process (720) fordetermining an end of life status may be initiated responsive thereto.

In some embodiments, detection of end of life may be achieved using acombination of methods that each individually detect of end of lifesignatures or risk factors. The combination of methods or signatures mayresult in improved specificity (e.g., low false positives). It should beappreciated that the end of life determination methods or algorithms canuse a combination of the risk factors in determining end of life.

In some embodiments, suitable risk factors may be selected from the listincluding, but not limited to: the number of days the sensor has been inuse (e.g., implanted); sensor sensitivity or whether there has been adecrease in signal sensitivity (e.g., change in amplitude and/orvariability of the sensitivity of the sensor compared to one or morepredetermined criteria), including magnitude and history; noise analysis(e.g., EOL noise factors (skewness, spikiness, & rotations)), duration,magnitude and history, spectral content analysis, pattern recognition);oxygen (e.g., concentration and/or whether there is a predeterminedoxygen concentration pattern); glucose patterns (e.g., mean,variability, meal characteristics such as peak-to-peak excursion,expected vs. unexpected behavior such as after a meal if glucose is notrising as expected); error between reference BG values and EGV sensorvalues, including direction of error (whether BG or EGV is readinghigher as compared to the other); and measure of linearity of the sensor(or the lack thereof). Sensor linearity refers to a consistency of thesensor's sensitivity over a particular range of measurement (e.g.,40-400 mg/dL for glucose sensors). For example, when the sensor signalis reading low with low BG and high with high BG, linearity may beassumed vs. when the sensor signal is reading low with low BG but notreading high with high BG (not changing or increasing beyond a certainBG value), where non-linearity may be assumed (based on error betweenreference BG values and EGV sensor values).

One risk factor that may be useful in the determination of end of lifeis the number of days the sensor has been in use (e.g., implanted). Theprocessor module may be configured to determine how many days the sensorhas been in use (e.g., implanted). In some embodiments, the number ofdays the sensor has been in used is determined based in part on usinginitial calibration data, sensor initialization, operable connection ofthe sensor with sensor electronics, user entered data, or the like. Insome embodiments, the processor module detects sensor restart and usesrestart information in the determination of the days since implantation.

In some embodiments, when a certain threshold has been met, e.g., acertain number of days, the particular variable associated with thethreshold may be automatically used in the end of life function. Forexample, if the number of days the sensor has been in use is determinedto be at least 4 days, then the number of days the sensor has been inuse is automatically used and/or a simple yes/no indicator that thethreshold has been met. In some embodiments, if the number of days thesensor has been in use is at least ⅓ of the days the sensor is approvedfor use, then the number of days the sensor has been in use isautomatically used. In other embodiments, if the number of days thesensor has been in use is at least ½, ⅔, or ¾ of the days the sensor isapproved for use, or the like, then the number of days the sensor hasbeen is automatically used. In some embodiments, the actual number ofdays the sensor has been in use is always used in the end of lifefunction. In some embodiments, the end of life function is performedafter a predetermined number of days of sensor use.

Additionally or alternatively, time elapsed from insertion may be mappedto an end of life risk factor value (e.g., likelihood of recovery orprobability of sensor failure in future) because the longer a sensor hasbeen in use since implantation, the more the sensor-tissue interfacechanges (bio-fouling) will likely impact sensor function. Translation ofend of life risk factors into values will be discussed in greater detailin reference to block 720 and/or FIG. 10. In one example, the end oflife risk factor value is mapped to about 1.0 between days 1 and 5 andreduces gradually beyond day 5 reaching to 0.5 at day 8, 0.2 at day 10,and about 0.1 at day 14. Other values and thresholds may be used as maybe appreciated by a skilled artisan.

Another risk factor that may be useful in the determination of end oflife is sensor sensitivity or whether there has been a decrease insignal sensitivity (e.g., change in amplitude and/or variability of thesensitivity of the sensor compared to one or more predeterminedcriteria), including magnitude and history. In some embodiments, theprocessor module may be configured to determine if there has been a dropin signal sensitivity. For example, for some sensors, their sensitivitydrifts up or remains relatively flat over most of the life of thesensor, e.g., 3, 5 or 7 days. Towards the end of life, the sensitivityof the sensor to changes in glucose may decrease. This reduction may berecognized as a drop in sensitivity that occurs monotonically overseveral hours (e.g., 12 hours), either by determining: (a) a change insensitivity (e.g., m in raw_signal=m*glucose+baseline) or (b) areduction in sensor raw count signal. For example, the followingequation may be used:

If median (raw count over last 12 hours)−median (raw count over last12-24 hours)<2*standard deviation over the last 12 hours, then thesensor may be nearing end of life.

In some embodiments, other forms of signal descriptive statisticsrelated to signal sensitivity (e.g., median, percentiles, inter-quartileranges, etc.) may be used to detect end of life. In some embodiments,whether there has been a decrease in signal sensitivity involves adetermination that compares a measured signal sensitivity against apredetermined signal sensitivity threshold or profile to determine ifthe measured signal sensitivity is within an acceptable range. Theacceptable range may be based on a priori information, such as fromprior in vitro and/or in vivo testing of sensors. In some embodimentsthe measured signal sensitivity is outside an acceptable range, then thesignal sensitivity may automatically be used in the end of lifefunction. In some embodiments, the measured signal sensitivity, a changein sensitivity and/or an indicator of a predetermined sensitivitydecline may be used as an input or a variable in the end of lifefunction.

In some embodiments, the sensitivity variable in the end of lifefunction is based on a trend of sensitivity during a particular sensorsession (e.g., during the life of the sensor in the host). For example,the determination of whether there has been a decrease in signalsensitivity includes comparing a first measured signal sensitivity at afirst time point against a second measured signal sensitivity at asecond time point to determine if rate of change in the measured signalsensitivity is within an acceptable range. The acceptable range may bedetermined by a priori information, such as from prior in vitro and/orin vivo testing of sensors. In one example, a change of greater than 20%over one day may be an indicator of end of life and useful as an inputin the end of life detection function. In one example, a rate ofacceleration (e.g., rate of drop of sensitivity) of greater than 20%over 12 hours may be an indicator of end of life and useful as an inputin the end of life detection algorithm.

In some embodiments, the rate of change of signal sensitivity may bedetermined based in part on a slow moving average of raw sensor data(e.g., counts). This embodiment takes advantage of the fact that formost patients, the average glucose over time (e.g., a few days or more)remain relatively constant; thus, a change in the average of the sensordata (e.g., uncalibrated (raw or filtered) over time (e.g., 2, 3, 4, 5,6, 7 days or more) may be interpreted as a change sensitivity of thesensor over time. The results of the slow moving average could be aquantifiable amount and/or simple yes/no indicators of a sensitivitydecline that may be useful as one input or variable into the end of lifefunction.

For example, the processor module may use an average of the last x hours(e.g. for 24 hours), a rectangular window averaging or an alpha filterwith an exponential forgetting factor to compute the slow moving averageto evaluate sensor sensitivity over time. In one example of an alphafilter with exponential forgetting, ‘alpha’ may be used as follows:

parameter(n)=parameter(n−1)*(1-alpha)+new_info*alpha

wherein alpha defines how much of history one wants to remember (howsoon to forget). If alpha is 0.01, then in 1/0.01 (i.e., time constantof 100) samples, 63% of previous information is forgotten. Accordingly,if a sampling rate is 12 samples/hr, then 63% of the signal would beforgotten by 100 samples, e.g., ˜8 hours. In such example, it wouldfollow that with 3 time parameters or constants, which is about 1 day,only 5% (i.e., 0.37*0.37*0.37=0.05) of signal left from previous daywould remain. In the above equation, alpha is a “forgetting factor.”Alpha may vary between 0 and 1, and its value dictates how fast oldmeasurements are forgotten by the model. For values of alpha close to 1,the model adapts more quickly to recent measurements. For values ofalpha close to 0, the model adapts more slowly to recent measurements.The value of alpha may depend on the elapsed time since the sensor wasimplanted. The calculation may be recursive or non-recursive.

In some embodiments, sensitivity loss may be indicative of end of life.Sensitivity loss may occur towards the sensor end of life due tophysiological wound healing and foreign body mechanisms around thesensor or other mechanisms including reference electrode capacity,enzyme depletion, membrane changes, or the like.

In some embodiments, sensor sensitivity may be computed in using ananalysis of uncalibrated sensor data (e.g., raw or filtered). In oneexample, a slow moving average or median of raw count starts showingnegative trends, the sensor may be losing sensitivity. Loss ofsensitivity may be computed by calculating a short term (e.g. ˜6-8hours) average (or median) of the sensor output and normalizing it bythe expected longer term (48 hours) average sensor sensitivity. If theratio of short term to long term sensitivity is smaller than 70%, theremay be a risk of sensor losing sensitivity. Loss of sensitivity may betranslated into an end of life risk factor value, for example a value ofabout 1 until the ratio is about 70%, reducing to 0.5 at 50% and <0.1 at25%.

FIG. 11A shows a sensor signal with a loss of sensitivity (downwardslope of signal) showing end of life symptoms (x-axis: time in hours,y-axis: sensor signal in counts). From the figure, it can be seen thatat about hour 170 (day 8), the sensor signal became significantly noisywith large downward spikes and gradually decreasing amplitude over thenext several hours to days due to loss of sensitivity. The end of lifedetection algorithm identified these two risk factors. For loss ofsensitivity, the algorithm first computed a maximum value of countoutput by the sensor over the last few days and used this max value asthe final steady state sensitivity of the sensor. The algorithm comparedthen calculated the short term (average of last 12 hours) average count.When the short term average counts were lower than the normal variationduring 8 hours (i.e., 2 times standard deviation in last 8 hours), thenthe algorithm flagged the sensor as at risk for end of life. The end oflife risk factor value was computed as follows: If short term average iswithin 80% of long term average, end of life risk factor value was 1. Asthe ratio of short to long term went below 0.6, the end of life riskfactor value was less than 0.5 and reached close to 0.1 if the ratiogoes below 0.4. Alternative computations for risk of end of life relatedto sensitivity may use external references such as glucose finger stickreadings. In either case, specific estimated sensitivity loss may betransformed into end of life risk factor values using functionsdescribed elsewhere herein.

In some embodiments, sensor sensitivity may be computed by comparingsensor data (e.g., calibrated sensor data) with reference blood glucose(BG). For example, calibration algorithms adjust the glucose estimatesbased on the systematic bias between sensor and a reference BG. End oflife algorithms may use this bias, called error at calibration ordownward drift, to quantify or qualify end of life symptoms. The errorat calibration may be normalized to account for irregular calibrationtimes and smoothed to give more weight to recent data (e.g., movingaverage or exponential smoothing). In some embodiments, end of life riskfactor value is determined based on the resulting smoothed error atcalibration. In such embodiments, end of life risk factor value is 1 forall values of error at calibration >−0.3, and reduces to 0.5 at error atcalibration=−0.4, and to <0.1 for error at calibration=−0.6.

Another risk factor that may be useful in the determination of end oflife is end of life (EOL) noise based on a noise analysis e.g., EOLnoise factor (skewness, spikiness, & rotations), duration, magnitude andhistory, spectral content analysis, pattern recognition, etc. In someembodiments, the processor module may be configured to evaluate thenoise (e.g., amplitude, duration and/or pattern) to determine if thereis a predetermined noise pattern indicative of EOL. For example, typicalsensor end of life signature may include an increase in spike activity,which can be detected using various methods of spike detection (e.g., bycomputing the mean rate of negative change).

In some embodiments, the duration of the noise may be indicative of endof life. FIG. 15 illustrates a diagram showing noise duration associatedwith end of life. Some noise detection algorithms that may be useful aredescribed in further detail in U.S. Pat. No. 8,260,393, incorporatedherein by reference in its entirety. In some embodiments, the inputs tothe calculation of noise duration risk factor metric is the noisecategorization of sensor data. For example, each raw sensor count may becategorized as clean, light noise, medium noise or severe noise based onthe relative magnitude of sensor and filtered sensor counts and theirderivatives. The EOL algorithm described in block 710 may use thisinformation to translate severe noise duration (e.g., amount of sensordata that are in severe noise state) into a metric that reflects end oflife risk. An assumption behind calculation of this metric is thatsensor end of life manifests as episodes if continuous noise is detectedrather than intermittent noise of a few samples. Thus, end of lifealgorithm may penalize the longer duration noise more, such as shown inFIG. 15. Thus, at each sample time, total duration of noise up to thepoint is used to calculate the end of life risk factor value at thatpoint.

In some embodiments, whether there is a predetermined end of lifesignature (noise pattern) involves a determination that includesevaluating the measured signal using pattern recognition algorithms todetermine identify predetermined an end of life signature in the sensorsignal. For example, by comparing the measured sensor signal against anoise pattern characteristic of end of noise to determine if therecorded noise pattern is similar to the predicted noise pattern.

In other embodiments, the determination of whether there is apredetermined noise pattern (end of life signature) includes comparingthe measure signal against a predetermined noise pattern to determine ifthe recorded noise pattern is similar to the predetermined noisepattern. For example, the predetermined noise pattern may include aseries of specific negative spikes in a short time frame. Thepredetermined noise pattern may include an increase in spike activityfor a given time frame.

In one embodiment, threshold detection for rate of change may be used todetect upward or downward spikes. Spikes may be detected by as may beappreciated by one skilled in the art. For example, point to pointdifference and thresholding, sharpness filters, etc. For example, analgorithm or function may output a +1 for an upward spike and a −1 for adownward spike. Using this spike data time series, one may use eitherupward spike (positive) spike detection algorithms or downward(negative) spike detection algorithms or total spike detection (e.g.,positive or negative spike time series) algorithms.

In some embodiments, end of life detection using these spike detectionfunctions may be achieved using a negative threshold on the movingaverage of spike time series (e.g., 2 times negative spikes thanpositive) or a threshold (e.g. 3 or 4) on total spike activity showing a3 to 4 times increase in total spike activity. Other forms of spikedetection such as least squares acceleration filters may be employed. Insome embodiments, an end of life risk factor value may be determined tobe 1 for a value of a spike metric <1, and reduced to 0.5 for a spikemetric >2, and to <0.1 for spike metric >5, and so on.

FIG. 11B illustrates the use of a spike detector to detect an increasein downward spikes in a signal produced by a sensor, which is a specificrisk factor for end of life (x-axis, time in hours, left y-axis: sensorsignal in counts, right y-axis: output of spike detection filter). Insome embodiments, noise occurring during sensor end of life may showlarge (e.g., greater than about 30% drop in amplitude) downward spikes(spikes that have overall negative change from average) in the midst ofsome random noise. In order to detect this type of end of life noise,the spike detection filter first identified positive and negative spikesas those that have a point to point change in signal of more than 33%.Then negative spikes were identified by looking at the average signalwithin the last 30 min (6 samples) when compared to the last 12 samples.The output of these steps was a signal that is +1 for a negative spikeand zero elsewhere. The algorithm then computed a moving average of 2hours of this signal (zeros and ones) to obtain the final output shown.A value of greater than 2 is considered to be indicative of risk for endof life.

In addition to or alternatively, high frequency activity or patterns maybe used in end of life detection. For example, end of life signaturepatterns may show a significant increase in high frequency activity whena power spectral density (PSD) or a Fast Fourier Transform (FFT) isperformed on the sensor data. Normal glucose signal has very lowfrequencies (e.g., 0 and 1.8 mHz). Consequently, a high pass filter or aband pass filter may be used to detect the end of life patternassociated with high frequency activity.

FIG. 12A shows the power spectral density (PSD) of the sensor signalshown in 11A illustrating end of life symptoms. PSD is the energy withineach discrete frequency of the signal and was calculated using Matlabpwelch function. Other standard functions implementing FFT may also beused. The energy content of the signal is calculated over a window ofabout 5 hours, and exceeded the expected energy by more than 10 times(i.e., freq. >0.2 mHz), resulting in the algorithm identifying a riskfor end of life. The solid trace shows the PSD of the first 7 days ofthe signal when no end of life symptoms were seen and the dashed traceis the PSD when end of life symptoms were seen. FIG. 12B shows the sametype of graph as 12A, but with from a sensor signal that did not exhibitend of life symptoms. The sensor performed well, as indicated by nochange in energy content in higher frequencies even after seven days. Inthese examples, the PSD of the sensor signal was monitored continuously.In FIG. 12A, the measured PSD increased on Days 7-11 as compare to theexpected PSD (measured from the First 7 days), showing an end of liferisk factor; while in FIG. 12B, the measured PSD (days 7-11) trackedmore closely to the expected PSD (from first 7 days) showing nosignificant risk of end of life. Expected PSD may computed using apriori sensor knowledge (e.g., by using sensor data from the first fewdays of sensor life of the same or of a different patient or sensorsession). In some embodiments, an end of life risk factor value is 1 forsensors whose short term PSD (at specific frequencies above 0.2 mHz) isabout 1-2 times the expected PSD, reduces to about 0.5 if short term PSDis >5 times the long term and reduces further to <0.1 if short term PSDis close to 10 times expected PSD.

In some embodiments, a slow changing long-time scale average signal maybe used to normalize the data to enhance the reliability of detectionmethods, e.g., signal sensitivity or noise pattern. For example, byusing the following definitions:

Long_time_scale=long time (1-2 day) moving average or filtered rawglucose data

Signature=short term (˜4-6 hrs) filtered (any including spike detection)data

Normalized_Signal=Signature/Long_time_scale

Thresholds for normalized signal and duration constraints may be appliedto detect end of life signatures. Consequently, end of life may bedetected if:

Normalized_Signal>Threshold for greater than certain Duration.

In some embodiments, the threshold and duration may be optimized toachieve specific sensitivity and specificity. Alternatively, having ashort duration constraint may be used to detect oxygen noise instead ofend of life.

With reference to FIGS. 9 and 10, any noise detection algorithm(s) maybe used to quantify the duration of noise (740) of a certain amplitude,for example, as described in more detail in U.S. Pat. No. 8,260,393,which is incorporated herein by reference in its entirety. This noiseanalysis may also be used to determine whether to display or not displayglucose to a user. For severe noise (above a predetermined level), thealgorithm further may process the signal to determine the end of liferisk factor value base on this noise.

In some examples, EOL noise (block 750) may be determined to be sensorend of life specific based on various algorithms that evaluate known endof life failure modes identifiable on the signal. It may have large(>30% point to point drop) downward spikes, negatively skewed over theduration of an episode, with intermittent rapid rotations oroscillations, e.g., multiple peaks and valleys or number of derivativesign changes. Noise discrimination can use these features to identify ifa sensor shows end of life symptoms and depending on the magnitude andduration, can calculate the end of life risk factor value from anepisode, which may also be termed the noise factor.

Another risk factor that may be useful in the determination of end oflife is oxygen (e.g., concentration and/or whether there is apredetermined oxygen concentration pattern). For example, in someembodiments, the processor module may be configured to determine ifthere is predetermined oxygen concentration and/or trend or patternassociated with the oxygen concentration. Any oxygen sensor useful forquantifying an oxygen concentration may be useful here, separate from orintegral with the sensor. In an electrochemical sensor that includes apotentiostat, pulsed amperometric detection can be employed to determinean oxygen measurement. Pulsed amperometric detection includes switching,cycling, or pulsing the voltage of the working electrode (or referenceelectrode) in an electrochemical system, for example between a positivevoltage (e.g., +0.6 for detecting glucose) and a negative voltage (e.g.,−0.6 for detecting oxygen). In some embodiments, oxygen deficiency canbe seen at the counter electrode when insufficient oxygen is availablefor reduction, which thereby affects the counter electrode in that it isunable to balance the current coming from the working electrode. Wheninsufficient oxygen is available for the counter electrode, the counterelectrode can be driven in its electrochemical search for electrons allthe way to its most negative value, which could be ground or 0.0V, whichcauses the reference to shift, reducing the bias voltage such asdescribed in more detail below. In other words, a common result ofischemia will be seen as a drop off in sensor current as a function ofglucose concentration (e.g., lower sensitivity). This happens becausethe working electrode no longer oxidizes all of the H₂O₂ arriving at itssurface because of the reduced bias.

In some embodiments, a non-enzyme electrode or sensor may be used as anoxygen sensor. In an exemplary dual working electrode sensor, havingenzyme and no-enzyme working electrodes, the non-enzyme electrode may beused as an oxygen sensor by changing the bias potential from a positivevalue (e.g., 600 mV-800 mV) to a negative value (e.g., negative 600mV-800 mV). At this potential, dissolved oxygen is reduced and givesrise to a negative current through the non-enzyme electrode. In someembodiments, by switching the bias potential on the non-enzyme electrodebetween the indicated positive and negative biases, a bi-functionalelectrode results. When a positive bias is applied, the current may berelated to baseline and when a negative bias is applied, the current maybe related to the local oxygen concentration.

It is known that glucose oxidase based sensors are limited by the amountof oxygen present. When the oxygen level reduces below a thresholdvalue, the enzyme electrode current drops (“oxygen starvation”) whilethe glucose concentration remains constant. This oxygen starvation mayresult in reduced accuracy, as lower than actual glucose levels may bereported. Oxygen starvation can occur late in sensor life, such as whenthe sensor is encapsulated in the subcutaneous environment.Consequently, being able to measure oxygen allows the detection of thisencapsulation and end of life for the sensor.

In some embodiments, whether there is a predetermined oxygenconcentration pattern involves a determination that includes reviewingthe oxygen concentration pattern to see if the oxygen concentration isappropriate. For example, an oxygen concentration pattern that showsreduction in oxygen availability over time may be indicative of end oflife of the sensor.

Another risk factor that may be useful in the determination of end oflife is glucose pattern (e.g., mean, variability, meal characteristicssuch as peak-to-peak excursion, expected vs. unexpected behavior such asafter a meal if glucose is not rising as expected).

Still another risk factor that may be useful in the determination of endof life is error between reference BG values and correspondingcalibrated sensor data (estimated glucose value, or EGV), includingdirection of error (e.g., whether BG or EGV is reading higher ascompared to the other) and/or utilizing flagged outliers, as describedin more detail elsewhere herein. In some embodiments, the processormodule may be utilized to identify discrepancies between referencevalues (e.g., BG) and sensor values (e.g., EGV). For example, asdiscussed above in the outlier detection, when there is a largedifference in the reference values and sensor values, something islikely not working correctly. In certain embodiments, a largediscrepancy between the reference values and sensor values may indicateend of sensor life. While not wishing to be bound to any particulartheory, this is believed because the sensor is reading either higher orlower than it should. In some embodiments, the direction of the error,for example whether the BG is higher or lower than the EGV is used as anend of life indicator as described also with reference to block 710.Still another risk factor that may be useful in the determination of endof life is a measure of linearity of the sensor (or the lack thereof).As described above, sensor linearity refers to a consistency of thesensor's sensitivity over a particular range of measurement (e.g.,40-400 mg/dL for glucose sensors).

In some embodiments, the processor module is configured to evaluate thevarious risk factors to provide end of life risk factor values, whichmay include simple binary (yes/no) indicators, likelihood or probabilityscores (e.g., relatively scaled or percentages) and/or actual numbers(e.g., outputs of the various tests). The risk factor values may bescaled if the weights used in the algorithm are modified.

In some embodiments, the processor module is configured to runprobability functions to determine a probability of end of life and/or alikelihood of recovery for one or more of the plurality of end of liferisk factors. In some embodiments, risk factors are mapped to a score(e.g., from 0 to 1) based on one or more parameters. The score may bemapped by functions, such as illustrated in FIGS. 13A and 13B, whichtranslate a particular risk factor or set of risk factors to an end oflife risk factor value, indicating for example, a possibility of thesensor to recover from a particular risk factor from end of life. Othermethods of translating risk factor outputs into end of life risk factorvalues may be used as is appreciated by a skilled artisan, such as byusing one or more criteria, algorithms, functions or equations.

In some embodiments, risk factors are fuzzified using pre-determinedmembership functions in order to quantify their propensity to indicateend of life. As used herein, a membership function defines the degreesto which a condition is satisfied, or a degree to which a value belongsto a fuzzy set defined by that function. In binary logic, a number wouldeither satisfy a condition fully or not at all; in fuzzy logic, a numbercan satisfy a condition to a certain degree described by a membershipfunction.

As an example of a binary indicator function, a noise level is comparedto a hard threshold, such as “5”; any value below 5 (such as 4.9) istreated as being noise-free and any value above 5 (such as 5.1) istreated as having an unacceptable level of noise. As an example of afuzzy membership function, a sigmoidal shape may be used to define asmooth transition in the evaluation of the noise levels. The inflectionpoint of the curve is set at 5, so there is no discontinuity at thatpoint. Thus, the same values of noise (4.9 and 5.1) as above are nowtreated very similarly. Fuzzification is the determination of the degreeto which a value belongs to a fuzzy set defined by a particularmembership function.

In some embodiments, each of the plurality of risk factors is partiallyindicative of the end of life of the sensor if each variable isdetermined to meet a threshold. In some embodiments, if at least two ofthe plurality of risk factors are determined to meet a threshold, thenthe combination of the at least two risk factors is indicative of theend of life of the sensor.

At block 720, the processor module is configured to determine an end oflife status. In one embodiment, a likelihood or probability analysis maybe used to determine an end of life status of the sensor. The outputs ofthe risk factors evaluated at block 710 become inputs into the end oflife determination process (block 720). As described in more detailelsewhere herein, the outputs of the risk factors of 710 may be mappedto end of life risk factor values, for example values from 0 to 1,probability or likelihood scores, actual values (outputs from the riskfactor evaluation(s)), and/or the like. The end of life risk factorvalues then become inputs into the end of life determination function,whereby the risk factors may be weighted or otherwise processed by theprocessor module using a probability analysis, decision matrix, varioussubroutines or the like, to determine an actual end of life indicator, aprobability (or likelihood) of end of life, a predicted time to end oflife, or the like. Probability functions, decision functions, varioussubroutines, or the like may be implemented as the end of lifedetermination function as is appreciated by one skilled in the art.

In one embodiment, decision fusion may be used as the function throughwhich the various inputs are processed (e.g. into block 720). Decisionfusion may provide a Fused Bayesian likelihood estimate based onsensitivity and specificity of individual detector algorithms associatedwith each input or variable. Suitable risk factors are measured, asdescribed in more detail above with reference to block 710, and fusedtogether to determine whether or not a sensor has reached end of life(EOL). A decision can be made for “yes” EOL or “no” EOL based on eachindividual risk factor. For example, if sensor sensitivity has decreasedby more than Δm over some amount of time Δt then “yes” EOL otherwise“no”, or if the sensor has had severe noise (above a predeterminedthreshold level) for more than 12 hours of the last 24 hours then “yes”EOL, otherwise “no”.

The individual decisions can be combined into a single Bayesianlikelihood value that can be used to make the best final decision aboutEOL, using the sensitivity and specificity of each variable in detectingEOL. First, each decision is converted to a likelihood value using thefollowing equation:

${\lambda (d)} = \frac{P( {dH_{1}} )}{P( {dH_{0}} )}$

where d is a binary decision of 0 or 1 (no or yes), H₁ is the case thatEOL is present, H₀ is the case that EOL is not, and P( ) is theprobability function. In practice, this means for a “yes” decisionλ=sensitivity/(1−specificity), and for a “no” decisionλ=(1−sensitivity)/specificity. For an individual variable test with highsensitivity and specificity, λ will be very high for a decision of 1 andvery small for a decision of 0.

In some embodiments, the individual likelihood values are multipliedtogether for a final fused likelihood value that takes into account theability of each individual variable to separate EOL from non-EOL. Thus,more sensitive and specific tests will be given greater weight in thefinal decision. A threshold may be determined empirically for the finalfused likelihood values to achieve the best separation of EOL andnon-EOL.

In some embodiments, linear discriminant analysis (LDA) may be used asthe end of life determination function, by taking the input variablesand providing an output decision.

In some embodiments, when EOL inputs or variables are fuzzified usingpre-determined membership functions, resulting degrees of membership forall data quality metrics are scaled according to pre-determined weightsand combined to produce an indicator of the overall quality of thecomputed glucose value. The weights may be applicable to every metricand may show how indicative a metric is of end of life. Theseembodiments may use several fuzzy logic concepts such as membershipfunctions and fuzzification, as described above, to determine the degreeof severity of each data quality metric. It should be understood thatthe fuzzification and membership functions can be applied to theprocessor module. The result of the end of life detection (block 720)may be a confidence indicator that determines a likelihood of end oflife beyond a simple pass/fail criterion.

In some embodiments, the processing moves immediately to block 730 toprovide an output related to (e.g., associated with and/or responsiveto) the end of life status determined at 720 (e.g., alert the user thatsensor is at its end of life or is predicted to end at a certain timepoint). In some embodiments, end of life status may be determined basedon likelihood of a sensor not recovering from an event rather thanoccurrence of an event; the likelihood of a sensor not recovering may bedefined as the state when a sensor is likely to be no longer accurate orhas long episodes of noise (e.g., based on risk factor evaluation(s)).The end of life indicator may also indicate a possibility of recovery(e.g., when the episode may be transient rather than terminal). In someembodiments, the processor module is configured to determine alikelihood of recovery and/or monitor the sensor or sensor data over thenext x hours to determine whether the sensor may recover from the end oflife symptoms (e.g., the likelihood of sensor providing accurate data touser in next 24 hours). In some embodiments, the sensor will only bedetermined to be at end of life if a high probability of sensor nottracking glucose in the future (e.g., 24 hours) or not showing glucoseat all for several hours (e.g., 12 hours) is determined (e.g.,inaccuracy may be determined by a comparison of EGV with reference BGusing a standard (e.g., within 20% or 20 mg/dL)).

Integral to or after block 720, the processor may optionally beconfigured to monitor the risk factors (e.g., for example morefrequently after end of life indicator determines a likelihood of end oflife) to determine whether it is more than likely that the sensor willnot recover from the end of life determination. Functions or algorithmssuitable for determining whether a sensor will recover from EOL may beselected from those known by one of skill in the art. For example,determining whether a sensor will recover may be a 0 to 1 scaling basedon an evaluation of one or more risk factors.

In some embodiments, the processor may be configured to determine, basedon recent history, the likelihood of a sensor to recover from the end oflife determination. For example, the EOL determination function maydetermine the end of life status is more than likely if there is a highprobability that the sensor will not track glucose in the future or thatthe sensor is not detecting glucose at all for extended durations.Extended durations may include time periods exceeding 12 hours. In someembodiments, the processor module is configured to suspend display ofsensor data during verification or determination of a likelihood ofrecovery, after which the processor module may be configured to eitherre-allow display of sensor data if it is determined that the sensor hasrecovered from the end of life symptoms. If, however, it is determinedthat more than likely the sensor will not recover from the end of lifesymptoms, the processing moves to block 730.

At block 730, the processor module may be configured to provide anoutput related to the end of life status of the sensor. For example, amessage related to the end of life status may be provided to a user(e.g., via a display). In some embodiments, the message to the user isprovided on the sensor display itself. In other embodiments, the messageis provided to a remote device, such as a laptop or mobile phone (e.g.,smartphone). In some embodiments, end of life status (e.g., orinformation related thereto) is transmitted to an insulin deliverydevice, for example, closed-loop algorithm that controls an insulinpump. For example, the end of life status or score may be compared toone or more criteria that dictates the processing or output responsivethereto.

It should be appreciated that a goal of end of life detection is toreduce the risk to performance occurring after the sensor has been inuse for many days. In some embodiments, the user may be notified. Forexample, if end of life shut off occurs after day 5 (e.g., of a 7-daysensor), the user may be notified twice. For example, an alarm may betriggered immediately after BG entry.

In some embodiments, advantages of detecting end of life for a sensorincludes: recognition of sensor failure would enable replacement of thesensor so that accuracy of glucose estimation is not significantlyimpacted, end of life pattern recognition may be used in preventingclosed-loop systems (e.g., artificial pancreas) from incorrect dosing,and extended use of sensor beyond the designed life of sensor may berecognized. As explained above, replacement of sensor so that accuracyof glucose estimation is not significantly impacted is desirable becauseit allows the user to rely on sensor readings with confidence.Similarly, recognition of extended use of sensor is desirable because itallows the sensor system to adjust the expectations or glucoseestimation according to known contributors (e.g., drift, oxygenreduction, etc.) or to advise the user that they are not using thesensor in accordance with e.g., FDA regulations, or to prompt the userto change the sensor (e.g., by providing a message to the user), or todeactivate/disable the sensor, etc., as described in more detail below.

In some embodiments, intermittent signs of end of life may be used toturn on advanced signal filtering techniques. Such filtering techniquesare described, for example, as described in more detail in U.S. Pat. No.8,260,393, which is incorporated herein by reference in its entirety.

In some embodiments, a message provided to the user related to the endof life status includes instructions to change the sensor if the end oflife is determined to occur within a predetermined time frame. Thepredetermined time frame may be based on the predicted time to end oflife, based on a predetermined time period of about 1, 2, 3, 4 hours ormore, or the like. In other embodiments, a message related to the end oflife status includes a warning that the sensor will shut down in apredetermined time frame if the end of life is determined to haveoccurred. The predetermined time frame may be immediately or within afew minutes or a few hours.

In other embodiments, the output provided in block 730 may include aninstruction, command, or set of instructions to suspend what is beingshown on the display (e.g., to the user or host) to avoid showinginaccurate data on the display based on the end of life score or statusas compared to a criterion, for example.

In still other embodiments, the output provided in block 730 may includean instruction, command, or set of instructions to shut the sensor down,disable display of real-time sensor data, instruction a user to removethe sensor, or the like, based on the end of life score or status ascompared to a criterion, for example. In some embodiments, theinstruction may shut off the display and/or shut off the monitor itself.Advantageously, the system is configured to not allow the user torestart the session until a new sensor insertion has been verified.

Referring now to FIG. 10, a flowchart showing example processesassociated with the flowchart of FIG. 9 is presented. In someembodiments, the goal of the end of life (EOL) algorithm is to assess asensor's ability to be accurate and reliable in the future. In thisexemplary embodiment, the algorithm monitors noise in sensor data andoffset from a BG (error at calibration), translates them into riskfactor values and determines the end of life status from currentcondition state (e.g., noise or large error state).

As illustrated in FIG. 10, inputs to the algorithm may include: durationof noise (740), EOL noise (750) and a downward drift of sensorsensitivity (760) determined based on analysis of error at calibration.These inputs can be iteratively determined or trended over time and theresults used in the combined algorithm of block 770. In this example,outputs of for each of the risk factor evaluations may be translatedinto end of life risk factor values based on current condition or state(e.g., values between 0 and 1), which are fed into combined algorithm ofblock 770.

As explained in detail above, there are multiple risk factors associatedwith end of life, including for example: (1) Day of sensor life, e.g.,time from insertion, (2) Noise amplitude and duration, (3) Noise type,and (4) Sensitivity Loss. Described herein are how these risk factorsmay be quantified and likelihood of sensor recovery from theseconditions computed. It should be appreciated that the risk factorsidentified in blocks 740, 750, and 760 may all be evaluated in block 710of FIG. 9.

In this example, the end of life algorithm has been selectively turnedon at a predetermined time (e.g., day 5 after implant), rather than thetime since implant being used as an input into the end of lifedetermination algorithm (770). However, the time since implant couldfurther enhance the ability of the end of life algorithm to accuratelydetermine end of life since the longer the sensor is implanted, the morelikely the sensor will reach end of life.

In this example, a noise detection algorithm, as described in moredetail in U.S. Pat. No. 8,260,393, which is incorporated herein byreference, is used to quantify the sensor data as clean or noisy (light,medium or severe) based on the amplitude of noise and the differencebetween raw sensor and filtered sensor signal. Noise duration (740) isdetermined based on the length of noise of a certain severity. Whennoise episodes of a certain severity (predetermined level of noise) oflonger than 2 hours occur, the likelihood of recovery is impacted. FIG.13A illustrates a translation function that maps a noise duration valueto an end of life risk factor value (e.g., from 0 to 1) in oneembodiment. Translation functions may be in the form of a function thatassociates a likelihood of recovery with a duration noise, or may useother mapping techniques, such as look up tables, various subroutines,and/or the like. Although some sensors experience episodes of noise,episodes lasting less than 2 hours of continuous noise may notnecessarily be indicative of end of sensor life. Thus, in the exampleillustrated in FIG. 13A, any continuous noise duration below 2 hours mayhave a very high value (e.g., risk factor value of 1) indicating a highlikelihood of recovery from the noise episode. As the duration increasesthe likelihood of the sensor being normal decreases as shown. At about 5hours the likelihood of the sensor being normal decreases to about 50%(e.g., 0.5 risk factor value) and at greater than 8 hours, the riskfactor value may translate to about 0.1, for example. In someembodiments, the end of life risk factor value is calculatedcontinuously for some period of time, and the resulting risk factorvalues may be smoothed, for example, for example using exponentialforgetting, or the like. Duration may be tracked cumulatively over asensor session, on a daily basis (e.g., hours/day), consecutively, orthe like.

EOL noise (block 750) is sensor end of life specific based anddetermined based on algorithms that evaluate the various aspects of thesensor signal related to noise: skewness of a short duration (e.g., 2hours) of noise, average rate of negative change of signal within thisepisode, and the number of peaks and valleys in the episode (number ofrotations), for example. Once these parameters are calculated, a noisefactor (e.g., between 0 and 1) is calculated by combining each parameteras may be appreciated by one skilled in the art. The parameters and/orthe EOL noise factor may be smoothed, for example using an exponentialforgetting factor. The EOL noise factor may be translated to an end oflife risk factor value, similar to FIG. 13A, for example, where a noisefactor close to zero translates to an end of life risk factor of 1,while a noise factor>0.5 has <0.5 end of life risk factor. These valuesmay be scaled if the weights used in the algorithm are modified.

Sensitivity Loss or Drift (block 760) is determined based on analysis ofan error at calibration (or receipt of reference value). In thisexample, the error at calibration is calculated by taking the differencebetween calibrated sensor data and time corresponding reference bloodglucose data, and dividing the result by the time since the continuousblood glucose monitor was last calibrated, whereby an indication ofdownward drift in sensitivity may be inferred, wherein calibrated sensordata is based on sensor data that has already been converted using aprevious calibration (conversion function). In this example, the errorat calibration is normalized and smoothed. In this example, thelikelihood of recovery is estimated based on the resulting smoothederror at calibration as illustrated in FIG. 13B, which is a translationfunction that maps the error at calibration to an end of life riskfactor value. FIG. 13B illustrates a translation function that defines amapping from an error at calibration to a risk factor value. In thisexample, when the smoothed error at calibration is less than −20%, therisk factor value is high (closer to 1.0). However, when the error atcalibration decreases to below −30%, the risk factor value rapidlydecreases and reaches about 0.1 when error is below −50%. This error isasymmetrical because sensor sensitivity tends to decrease as itapproaches it end of life.

The processor module may then take the risk factor values (740, 750,760) and combine them into a metric (e.g., a weighted average) or EOLindex (block 770), the result of which is then compared against end oflife criteria at block 780 to determine an end of life status. FIG. 13Cillustrates a translation function that may be used by the processormodule to translate the combined EOL index calculated from the variousrisk factors to an end of life score and/or end of life status, in oneembodiment. In this example, after obtaining the various risk factorvalues, they were summed using a weighted average to obtain the combinedEOL index. This risk likelihood index was normalized by the sum of theweights as shown in FIG. 13C. An end of life score close to 1 isindicative of a high likelihood of recovery from the various EOLsymptoms (risk factors), but a value close to zero indicates a goodpossibility or probability that the sensor has reached the end of itslife.

It should be appreciated that blocks 770 and 780 may collectively occurin block 720 of FIG. 9. Please note that for all above risk factors,risk factor values may be computed from look up tables or functionsdeveloped from a priori knowledge of sensors and their behavior overtime. Once these risk factors may be combined into a metric (e.g., aweighted average) or EOL index (block 770), using a suitable algorithm,which is then used to determine an end of life status of the sensor(e.g., if a sensor is likely to be useful and accurate in the future) atblock 780 in FIG. 10. If, for example, the overall weighted average ofthe EOL Index is close to 1, then the sensor is likely to recover fromcurrent condition and is likely to be accurate and useful in the future(e.g., next 24 hours). If the weighted average is <0.5, then the sensoris not likely to be useful in the future. This value may be used to takedifferent actions (e.g., provide different outputs at block 730 of FIG.9).

In another example of how the various risk factors can be combined todetermine sensor end of life; the processor module may begin bydetermining that it is day 5 and evaluate a plurality of blood glucoseentries over time to determine end of life. In accordance with block710, the processor module evaluates a plurality of blood glucose entriesover time and the number of days the sensor has been in use. Forexample, for a first blood glucose entry (BG1), if the sensordata<<reference data (BG1) by more than a predetermined amount (e.g.,outside of a 40% or 40 mg/dL difference), one or more resulting valuesmay be considered by the end of life determination function.

If the end of life criteria is met for BG1, the sensor system may promptthe user for a second blood glucose entry (BG2). If BG2 confirms BG1 isan accurate blood glucose value (e.g., by being a comparable value),then the BG values are considered partially indicative of end of life.One skilled in the art can envision subroutines useful to evaluatedifferences in BG vs. EGV and map likelihood of recovery thereby.

As described above, the processor also considers the number of days thesensor has been in use. For example, the end of life determinationprocess may only begin after a particular day post implant and only whenoutlier detection identifies an outlier that meets certain criteria. Thenumber of days the sensor has been in use may be determined or derivedby examining the maximum possible sensor value based on calibrationhistory (day 4+). If the maximum sensor value still<<BG1 or BG2, thenthe number of days the sensor has been in use is considered partiallyindicative of end of life.

Taking into consideration the BG values and the number of days thesensor has been in use, end of life is determined at block 720, usingvarious subroutines, and the likelihood of recovery is determined ordetermined to be 0. Thereafter, the sensor system may provideinstructions to shut off the sensor, in accordance with block 730.

In some embodiments, certain assumptions may be made with the end oflife algorithm. For example, the blood glucose readings may be assumedto be accurate. If there are 2 errant blood glucose readings (e.g.,sensor<<BG) such as described above in the outlier detection/culling,then the blood glucose values may be partially indicative of end of life(such as described in the present example).

As yet another example of how the various risk factors can be combinedto determine sensor end of life, the processor module evaluates a signalfor a trend in noise (e.g., signal spikes) and a trend in sensorsensitivity and provides the risk factors to be processed by end of lifefunction or algorithm. This example is described below.

At block 710, the processor module compares the measured signal againsta predetermined noise pattern to determine if the recorded noise patternis similar to the predetermined noise pattern. For example, thepredetermined noise pattern may include a series of specific negativespikes in a short time frame. The predetermined noise pattern mayinclude an increase in spike activity for a given time frame. In thepresent example, the processor module identifies a 3× increase in signalspikes over the past 24 hours, which is a risk factor indicative of endof life.

Similarly, the processor module evaluates the sensitivity over time andidentifies a change in sensor sensitivity of more than 20% decline in 24hours, or compares to an expected profile, such as described withreferences to FIGS. 1 and 2, which is a risk factor indicative of end oflife. As described above, sensor sensitivity may be determined usinge.g., a raw count analysis or a comparison with a reference bloodglucose value. At block 720, the end of life function processes thespike trend and sensitivity trend using instructions or conditions todetermine whether end of life is, has or will occur, or to determine alikelihood of recovery. If the likelihood of recovery is less than 50%,then the sensor system may provide instructions to shut off the sensor,in accordance with block 730.

Sensor Reuse

In some embodiments, sensor reuse can be detected. This may be achievedusing the end of life sensor function and comparing the profile of aused sensor against what the profile of a new sensor should look like.Sensor reuse can be dangerous to the user because the sensor may provideinaccurate data upon which the user may rely.

FIG. 14 is a flowchart of an example process 900 for determining sensorreuse in accordance with an embodiment. At block 902, a sensor insertionevent is triggered. An insertion event can be one of any number ofpossible events that indicate a new sensor has been implanted, such as auser providing input to a sensor system that a new sensor has beenimplanted, the sensor system detecting electrical connection to asensor, a predetermined amount of time transpiring since the systemprompted a user to use a new sensor, and the like. Next, at step 904, adata point or series of data points are collected from the analytesensor being used, and an initial calibration is run using the collecteddata to produce an initial calibration at step 906. Thereafter, in block908, the data point or series of data points are used as inputs into theend of life sensor function. At block 910, the end of life functionproduces a sensor profile. The sensor profile may include a graphillustrating noise, sensitivity, etc., as described above with respectto e.g., FIGS. 11-13 and 15. At block 912, the sensor profile from block910 is compared against a standard sensor profile for a new sensor and asimilarity profile is calculated. At step 914, a decision is madewhether the calculated similarity profile is indicative of a new sensoror a reused sensor. If the similarity profile indicates a new sensor,example process 900 is ended at block 916. If the similarity profileindicates a reused sensor, a notification may be provided at block 918.

The sensor reuse routine of step 918 can include triggering an audibleand/or visual alarm notifying the user of improper sensor reuse. Thealarm can also inform the user why sensor reuse may be undesirable, suchas potentially providing inaccurate and unreliable sensor readings. Thesensor reuse routine 918 can alternatively or additionally cause thesensor system to fully or partially shut down and/or cease display ofsensor data on a display of the sensor system.

Exemplary Sensor System Configurations

Embodiments of the present disclosure are described above and below withreference to flowchart illustrations of methods, apparatus, and computerprogram products. It will be understood that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by execution of computer programinstructions. These computer program instructions may be loaded onto acomputer or other programmable data processing apparatus (such as acontroller, microcontroller, microprocessor or the like) in a sensorelectronics system to produce a machine, such that the instructionswhich execute on the computer or other programmable data processingapparatus create instructions for implementing the functions specifiedin the flowchart block or blocks. These computer program instructionsmay also be stored in a computer-readable memory that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture includinginstructions which implement the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks presented herein.

In some embodiments, a sensor system is provided for continuousmeasurement of an analyte (e.g., glucose) in a host that includes: acontinuous analyte sensor configured to continuously measure aconcentration of the analyte in the host and sensor electronicsphysically connected to the continuous analyte sensor during sensor use.In one embodiment, the sensor electronics includes electronicsconfigured to process a data stream associated with an analyteconcentration measured by the continuous analyte sensor in order toprocess the sensor data and generate displayable sensor information thatincludes raw sensor data, transformed sensor data, and/or any othersensor data, for example. The sensor electronics can include electronicsconfigured to process a data stream associated with an analyteconcentration measured by the continuous analyte sensor in order toprocess the sensor data and generate displayable sensor information thatincludes raw sensor data, transformed sensor data, and/or any othersensor data, for example. The sensor electronics can include a processorand computer program instructions to implement the processes discussedherein, including the functions specified in the flowchart block orblocks presented herein.

In some embodiments, a receiver, which can also be referred to as adisplay device, is in communication with the sensor electronics (e.g.,via wired or wireless communication). The receiver can be anapplication-specific hand-held device, or a general purpose device, suchas a P.C., smart phone, tablet computer, and the like. In oneembodiment, a receiver can be in data communication with the sensorelectronics for receiving sensor data, such as raw and/or displayabledata, and include a processing module for processing and/or display thereceived sensor data. The receiver can also and include an input moduleconfigured to receive input, such as calibration codes, referenceanalyte values, and any other information discussed herein, from a uservia a keyboard or touch-sensitive display screen, for example, and canalso be configured to receive information from external devices, such asinsulin pumps and reference meters, via wired or wireless datacommunication. The input can be processed alone or in combination withinformation received from the sensor electronics module. The receiver'sprocessing module can include a processor and computer programinstructions to implement any of the processes discussed herein,including the functions specified in the flowchart block or blockspresented herein.

It should be appreciated that all methods and processes disclosed hereinmay be used in any glucose monitoring system, continuous orintermittent. It should further be appreciated that the implementationand/or execution of all methods and processes may be performed by anysuitable devices or systems, whether local or remote. Any combination ofdevices or systems may be used to implement the present methods andprocesses.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. The functions, steps and/oractions of the method claims in accordance with the embodiments of theinvention described herein need not be performed in any particularorder. Furthermore, although elements of the invention may be describedor claimed in the singular, the plural is contemplated unless limitationto the singular is explicitly stated. Therefore, the description andexamples should not be construed as limiting the scope of the disclosureto the specific embodiments and examples described herein, but rather toalso cover all modification and alternatives coming with the true scopeand spirit of the disclosure.

What is claimed is:
 1. A system for determining if a continuous analytesensor has been reused, the system comprising sensor electronicsconfigured to be operably connected to a continuous analyte sensor, thesensor electronics configured to: evaluate a plurality of risk factorsassociated with end of life symptoms of the sensor; determine an end oflife status of the sensor by performing an end of life function based onthe evaluation of the plurality of risk factors; and provide an outputrelated to sensor reuse of the sensor within a predetermined time frameafter sensor initialization if the end of life status meets one or morepredetermined sensor reuse criteria, wherein the plurality of riskfactors comprise at least two risk factors selected from the groupconsisting of a number of days the sensor has been in use, a rate ofchange of sensor sensitivity, end of life noise, oxygen concentration,glucose patterns, error between reference values, and sensor values inclinical units.
 2. The system of claim 1, wherein one of the at leasttwo risk factors comprises a rate of change of sensor sensitivity, andwherein the sensor electronics are configured to evaluate a rate ofchange of sensor sensitivity by evaluating at least one of a directionof rate of change of sensor sensitivity, an amplitude of rate of changeof sensor sensitivity, a derivative of rate of change of sensorsensitivity, or a comparison of the rate of change of sensor sensitivityto a priori rate of change sensitivity information.
 3. The system ofclaim 1, wherein one of the at least two risk factors comprises end oflife noise, and wherein the sensor electronics are configured toevaluate end of life noise by evaluating at least one of duration ofnoise, a magnitude of noise, a history of noise, a spectral content of asignal from the sensor, spikes in the signal from the sensor, skewnessof the signal of the sensor, or noise patterns by pattern recognitionalgorithms.
 4. The system of claim 1, wherein one of the at least tworisk factors comprises end of life noise, and wherein the sensorelectronics are configured to evaluate end of life noise by evaluatingat least two of duration of noise, a magnitude of noise, a history ofnoise, a spectral content of a signal from the sensor, spikes in thesignal from the sensor, skewness of the signal of the sensor, or noisepatterns by pattern recognition algorithms.
 5. The system of claim 1,wherein one of the at least two risk factors comprises glucose patterns,and wherein the sensor electronics are configured to evaluate glucosepatterns by evaluating at least one of mean glucose, glucosevariability, peak-to-peak glucose excursions, or expected versusunexpected glucose trends based on timing.
 6. The system of claim 1,wherein one of the at least two risk factors comprises error betweenreference values and sensor values in clinical units, and wherein thesensor electronics are configured to evaluate error between referencevalues and sensor values in clinical units by evaluating at least one ofa direction of error between reference values and sensor values inclinical units, or a linearity of the sensor and an error atcalibration.
 7. The system of claim 1, wherein the sensor electronicscomprise a processor module, the processor module comprisinginstructions stored in computer memory, wherein the instructions, whenexecuted by the processor module, cause the sensor electronics toperform the evaluating and the providing.
 8. The system of claim 1,wherein the sensor electronics are configured to provide an output bydisabling display of sensor data responsive to the end of life statusmeeting the one or more predetermined sensor reuse criteria.
 9. Thesystem of claim 1, wherein the sensor initialization is determined bythe sensor electronics in response to an event that indicates a newsensor has been implanted, including one or more of: a user providinginput to a sensor system that a new sensor has been implanted, thesensor system detecting electrical connection to a sensor, apredetermined amount of time transpiring since the system prompted auser to use a new sensor.
 10. The system of claim 1, wherein the sensorelectronics are configured to collect a data point or series of datapoints from the analyte sensor being used, and wherein the evaluation ofa plurality of risk factors associated with end of life symptoms of thesensor comprises evaluation of the collected data point or series ofdata points.
 11. A method for determining if a continuous analyte sensorhas been reused, comprising: evaluating a plurality of risk factorsassociated with end of life symptoms of a sensor; determining an end oflife status of the sensor by performing an end of life function based onthe evaluation of the plurality of risk factors; and providing an outputrelated to a sensor reuse within a predetermined time frame after sensorinitialization if the end of life status meets one or more predeterminedsensor reuse criteria, wherein the plurality of risk factors comprise atleast two risk factors selected from the group consisting of a number ofdays the sensor has been in use, a rate of change of sensor sensitivity,end of life noise, oxygen concentration, glucose patterns, error betweenreference values, and sensor values in clinical units.
 12. The method ofclaim 11, wherein one of the at least two risk factors comprises a rateof change of sensor sensitivity, and wherein evaluating a rate of changeof sensor sensitivity comprises evaluating at least one of a directionof rate of change of sensor sensitivity, an amplitude of rate of changeof sensor sensitivity, a derivative of rate of change of sensorsensitivity or a comparison of the rate of change of sensor sensitivityto a priori rate of change sensitivity information.
 13. The method ofclaim 11, wherein one of the at least two risk factors comprises end oflife noise, and wherein evaluating end of life noise comprisesevaluating at least one of duration of noise, a magnitude of noise, ahistory of noise, a spectral content of a signal from the sensor, spikesin the signal from the sensor, skewness of the signal of the sensor ornoise patterns by pattern recognition algorithms.
 14. The method ofclaim 11, wherein one of the at least two risk factors comprises end oflife noise, and wherein evaluating end of life noise comprisesevaluating at least two of duration of noise, a magnitude of noise, ahistory of noise, a spectral content of a signal from the sensor, spikesin the signal from the sensor, skewness of the signal of the sensor ornoise patterns by pattern recognition algorithms.
 15. The method ofclaim 11, wherein one of the at least two risk factors comprises glucosepatterns, and wherein evaluating glucose patterns comprises evaluatingat least one of mean glucose, glucose variability, peak-to-peak glucoseexcursions, or expected versus unexpected glucose trends based ontiming.
 16. The method of claim 11, wherein one of the at least two riskfactors comprises error between reference values and sensor values inclinical units, and wherein evaluating error between reference valuesand sensor values in clinical units comprises evaluating at least one ofa direction of error between reference values and sensor values inclinical units, a linearity of the sensor, or an error at calibration.17. The method of claim 11, wherein the providing an output comprisesdisabling display of sensor data responsive to the end of life statusmeeting the one or more predetermined sensor reuse criteria.
 18. Themethod of claim 11, comprising determining sensor initialization inresponse to an event that indicates a new sensor has been implanted,including one or more of: a user providing input to a sensor system thata new sensor has been implanted, the sensor system detecting electricalconnection to a sensor, a predetermined amount of time transpiring sincethe system prompted a user to use a new sensor.
 19. The method of claim11, comprising collecting a data point or series of data points from theanalyte sensor being used, wherein the evaluating a plurality of riskfactors associated with end of life symptoms of the sensor comprisesevaluating the collected data point or series of data points.
 20. Themethod of claim 19, comprising performing an initial calibration usingthe collected data to produce an initial calibration.